Millimeter wave and ultrasonic sensors

Abstract

The present invention is entitled "Millimeter Wave and Ultrasonic Sensors". The present disclosure provides a fluid mixture parameter determination (FMPD) system for analyzing a fluid mixture while moving, the FMPD system comprising a computing system and at least one material model, the at least one material model comprising two or more model parameters for multiple material compositions stored in a memory. An ultrasonic sensor and a millimeter wave (MMW) sensor are each coupled to sense the fluid mixture and coupled to the computing system. The ultrasonic sensor is used to provide ultrasonic data to the computing system, including the velocity or volume flow rate of the fluid mixture, and the speed of sound (VoS) through the fluid mixture. The MMW sensor is used to provide MMW velocity data to the computing system. The computing system is used to use the material model together with the ultrasonic data and the MMW velocity data to identify parameters, including multiple components in the fluid mixture and the concentrations of the multiple components.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims the benefit of provisional application serial number 63 / 024,690, filed on May 14, 2020, entitled "FLUID PARAMETER DETERMINATION USING MILLIMETER-WAVE AND ULTRASOUND SENSORS," which is incorporated herein by reference in its entirety. Technical Field

[0003] The present disclosure relates to a parameter determination system for a fluid mixture. Background Art

[0004] Natural gas is a mixture of gas that contains primarily methane (CH4) and gas molecules with higher carbon atoms (usually containing 2 to 8 carbons), such as ethane, propane, and butane. Other gases, such as nitrogen, carbon dioxide, hydrogen sulfide, and helium, are also commonly found in natural gas. Natural gas may also include a variety of contaminants, such as sand, water, and oil. In this initial, unrefined stage, natural gas is often referred to as "wellhead quality" or "raw" natural gas.

[0005] There is a growing interest and need for online real-time quality inspection and monitoring of fluid mixtures while the fluid is flowing, such as natural gas flowing in a pipeline. The composition of gas transported through pipelines or liquefied natural gas (LNG) stored in ships is conventionally determined by gas chromatography (GC).

[0006] GC is an analytical technique for determining the composition of materials containing more than one constituent component, such as in the case of natural gas, to enable calculation of its energy content. Conventional GC requires a heater and a voltage regulator inside the instrument to process the sample. In North America, energy content is usually reported as British thermal units (BTU). The higher the BTU rating, the more valuable the fuel, and therefore the higher the price the fuel is marked. Therefore, natural gas producers, distributors, utilities, natural gas engine and appliance manufacturers, and consumers need analysis of the composition of natural gas. Summary of the invention

[0007] This Summary is provided to introduce a brief selection of disclosed concepts in a simplified form, which are further described below in the Detailed Description including the provided figures. This Summary is not intended to limit the scope of the claimed subject matter.

[0008] The disclosed aspects recognize that conventional gas chromatographs for analyzing the composition of fluid mixtures, such as natural gas, may be too expensive and complex to purchase and implement for small gas station and distribution customers. The disclosed aspects include a relatively low-cost fluid mixture parameter determination (FMPD) system configured for online (real-time) measurements, wherein the FMPD system includes at least a millimeter wave (MMW) sensor and an ultrasonic sensor, each of which provides sensor data to a computing system including at least one material model. The computing system uses the corresponding sensor data together with the material model to determine multiple parameters of the fluid mixture as the fluid mixture moves. The parameters determined include various components present in the fluid mixture and their corresponding concentrations, volume flow rate, and speed of sound (VoS), and optionally other parameters such as fluid density, humidity, mass flow rate, temperature, and pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1A A simplified depiction of an exemplary FMPD system according to one exemplary aspect is shown.

[0010] Figure 1B shows that now in the common shell Figure 1A The FMPD system is shown with the mounting flange on its end, and the function selector switch and display.

[0011] Figure 2 An exemplary data processing flow diagram for determining a plurality of fluid parameters of a flowing fluid mixture using the disclosed FMPD system is shown.

[0012] Figure 3 is an exemplary table showing material composition properties of various component materials, including those typically included in natural gas (shown as NG).

[0013] Figure 4 An example of the disclosed data fusion arrangement is shown, where data from MMW sensors, ultrasonic sensors, and FBG sensors are all processed together by a computing system implementing a Bayesian classification block, an artificial neural network (ANN) engine, and a mathematical model. DETAILED DESCRIPTION

[0014] The disclosed embodiments are described with reference to the accompanying drawings, wherein the same reference numerals are used to refer to similar or equivalent elements in all drawings. The drawings are not drawn to scale, and they are provided only to illustrate the aspects disclosed herein. Several disclosed aspects are described below with reference to exemplary applications for illustration. It should be understood that many specific details, relationships, and methods are set forth to provide a complete understanding of the embodiments disclosed herein.

[0015] Additionally, the terms "coupled to" or "coupled with..." (etc.) as used herein without further qualification are intended to describe either an indirect or direct electrical connection. Thus, if a first device is "coupled" to a second device, the connection may be through a direct electrical connection, where only parasitics exist in the path; or through an indirect electrical connection via intermediary items including other devices and connections. For indirect coupling, the intermediary items generally do not modify the information of the signal, but may adjust its current level, voltage level, and / or power level.

[0016] Although the numerical ranges and parameters showing the wide range of the present disclosure are approximate values, the numerical values ​​shown in the specific examples are reported as accurately as possible. However, any numerical value inherently contains certain errors, which are necessarily caused by the standard deviations present in its corresponding experimental measurements. In addition, all ranges disclosed herein are understood to cover any and all subranges contained therein. For example, a range of "less than 10" may include any and all subranges between (and including) a minimum value of 0 and a maximum value of 10, that is, any and all subranges having a minimum value equal to or greater than zero and a maximum value equal to or less than 10 (e.g., 1 to 5).

[0017] Figure 1A A simplified depiction of an exemplary FMPD system 100 is shown mounted on a pipe 180 for determining multiple parameters of a fluid mixture as the fluid mixture moves through the pipe 180 according to an exemplary aspect. The pipe 180 is shown as having a diameter d (which defines the cross-sectional area of ​​the pipe) with a fluid mixture therein, which is shown flowing from left to right at a velocity shown as V. The FMPD system 100 is suitable for online (real-time) monitoring of multiple parameters of a fluid mixture as the fluid mixture moves. The FMPD system 100 can be considered a "super meter" because it implements all in-line Figure 1A , and typically also has firmware that enables any of these functions and features to be enabled or disabled by an operator of the FMPD system 100.

[0018] These meters are shown as volume flow meter 101, mass flow meter 102, energy (calorific value) meter 103, online chromatograph and fluid quality analyzer 104, density meter 105, water content meter 106 (providing humidity measurement), and fluid pressure and temperature meter 107. Also shown is the computing system 120 of the FMPD system 100, which provides analyzers including composition concentration analyzer 108 and fluid complex dielectric constant analyzer 109.

[0019] The FMPD system 100 includes an MMW sensor 145 and an ultrasonic sensor 130, which is shown as having a pair of transducers T1 and T2. Although the ultrasonic sensor 130 is shown as having a pair of transducers, there may be more than 1 pair of transducers. Also shown are optional fiber Bragg grating (FBG) sensors 135 represented by rectangles, which are shown positioned opposite each other on the inner surface of the pipe 180. The FBG sensors 135 can be implemented in various ways, wherein the array of sensor elements of the FBG sensors 135 are generally mounted on a fiber optic ribbon that can be mounted along the inner circumference of the pipe 180. All of these sensors 145, 135, 130 are shown as having transmitting, receiving and sensing features, and are each attached to the pipe 180 to perform their respective sensing functions. Also shown are a pressure sensor 170 and a temperature sensor 175, which are attached to the pipeline 180 to perform their respective sensing functions.

[0020] The computing system 120 includes a processor 121 and at least one associated memory 122. The computing system 120 is shown as implementing a signature extraction block 123, a sensor data diagnostics, calibration and fusion block 124, a material model and parameter block 125, and a material parameter classification and identification block 126. The computing system 120 also implements an ANN engine or artificial intelligence (AI) engine, shown as an ANN / AI learning block 127, which is shown coupled to the material parameter classification and identification block 126.

[0021] The respective sensors 145, 135, 130 are shown coupled to the computing system 120 via a pre-processing block 155 and then an analog-to-digital converter (ADC) 150. The pre-processing block 155 provides the functions of data filtering, data scaling and time synchronization. Time synchronization is often required during measurement and for control. Time synchronization in the pre-processing block 155 is primarily used to align the data provided by the respective sensors with respect to a time axis and a spatial axis, which are sometimes converted into common coordinates, such as based on polar coordinates or Cartesian coordinates.

[0022] include Figure 1A The FMPD system 100 of the respective sensors 145, 135, 130 shown may all be provided together as a single universal sensor system (see Figure 1B , all of which are contained within a common housing shown as 190). Alternatively, the respective sensors 145, 135, 130 may be provided separately.

[0023] The MMW sensor 145 is used to emit high frequency (short wavelength) radiation and is sensitive to material composition. The high frequency / short wavelength enables a relatively small size antenna. The operating frequency of the MMW sensor 145 is generally 24 GHz to 300 GHz, where 24 GHz is sometimes also referred to as MMW or quasi-MMW. The bandwidth of the MMW sensor 145 is generally >10% of its center frequency, such as >30 GHz in the specific case of a center frequency of 300 GHz. The MMW sensor 145 provides MMW velocity data that is used to characterize the corresponding materials in the fluid mixture, including for use by the computing system 120 to measure the concentrations of various component materials of the fluid mixture, which is in Figure 1A Shown therein is a composition concentration analyzer 108 .

[0024] Also shown is a computing system 120 providing measures of the complex dielectric constant, density, water content, fluid pressure, fluid temperature, volume flow rate, mass flow rate, total energy of the fluid mixture, as well as the online chromatograph and fluid quality analyzer 104 .

[0025] Figure 1A The rectangular frame portion of the MMW sensor 145 shown generally includes a controller, such as a microcontroller, a digital signal processor (DSP), a transmitter, and a receiver. The MMW sensor 145 also includes at least one antenna, which is shown as a first triangle and a second triangle. Figure 1A Identified as A1 and A2 in FIG, they are configured as an antenna pair. As is known for MMW sensors, the transmitter includes a phase locked loop (PLL), a sweep frequency controller and a power amplifier, and the receiver includes a mixer and a low noise amplifier (LNA).

[0026] As high frequency semiconductor technology advances, miniaturized systems on a chip (SoC) are commercially available MMW sensors 145, such as those provided by Texas Instruments Incorporated. The MMW sensors 145 described above can provide bandwidths of 30 GHz and 300 GHz, which allows relatively complex characterizations to be performed using scattering matrices, polarization, frequency, phase, and amplitude signatures of gases (and possibly liquids and solids). Microwaves utilized by the MMW sensors 145 are considered a reliable non-destructive testing (NDT) technique for material moisture sensing and fluid concentration measurement.

[0027] The MMW velocity data provided by the MMW sensor 145 is used by a computing system 120 having a database shown as a material model and parameter block 125 to enable detection of corresponding components known to be in the fluid mixture, wherein Figure 3Exemplary material model data in the material composition property table shown is described below. Another material identification method is to use calibration for the unknown properties and components to be found in the fluid mixture, and then perform classification and material identification on the previously unknown components. In the case where the fluid mixture is natural gas, the corresponding materials may include known materials in natural gas, including moisture and major hydrocarbon gas composition components (e.g., mainly methane (C1), C2, C3 and C4, all of which are described below). Figure 3 ).

[0028] During operation of the MMW sensor 145, the MMW emitted by an antenna (such as A1) that acts as a transmitting antenna passes through the fluid mixture before reaching another antenna (such as A2) that acts as a receiving antenna. The speed of the MMW can be determined by the travel time between A1 and A2, where the signal shape, signal width and frequency of the MMW are changed by the presence of the fluid mixture. Different fluid mixture compositions will cause different effects, where the higher the relative dielectric constant and / or magnetic permeability of the fluid medium, the lower the speed at which the MMW wave travels through it.

[0029] The ultrasonic sensor 130 measures fluid parameters including the velocity and VoS of the fluid mixture, which is also referred to as the speed of sound (SoS) in the fluid. The VoS is primarily affected by the corresponding materials, temperature, and pressure in the fluid mixture. The ultrasonic sensor 130 also provides raw data to the computing system 120, which typically uses a dedicated algorithm to generate a signal signature including the shape, amplitude, and phase of the received raw data signal.

[0030] Pressure and temperature sensors are standard features provided by conventional ultrasonic sensors. The pressure and temperature readings from the fluid pressure and temperature gauge 107 are used by the ultrasonic sensor 130 to convert the measured real-time volume to a standard volume, where the conversion is typically performed by an electronic volume corrector (EVC) that is part of the ultrasonic sensor 130. Although the ultrasonic flow meter 130 typically has an integrated EVC, other turbine-type flow meters and rotary flow meters typically use a separate EVC.

[0031] The ultrasonic flow meter 130 has T1 and T2 at Figure 1A 180, each of which is generally configured to transmit or receive high frequency ultrasonic pulses that pass through the fluid mixture. Figure 1AThe rectangular box shown as ultrasonic sensor 130 in FIG. 1 includes a controller as well as a transmitter and receiver, and typically also includes a multiplexer connecting Tx and Rx so that at any given time, one transducer is transmitting while the other is receiving, and vice versa. The excitation pulse is generated in the transmitter chain, and there is typically also an automatic gain control (AGC) in the transmitter chain.

[0032] As is known in the field of ultrasonic sensing, other transducer arrangements are also possible, such as both the first transducer T1 and the second transducer T2 being positioned on the same side of the pipe 180, and more than two transducer ultrasonic sensing arrangements. There may also be a reflection path arrangement, where the first transducer T1 and the second transducer T2 may be on the same side of the pipe 180.

[0033] With regard to the sensors and corresponding sensing techniques for the ultrasonic sensor 130, although typically operated at relatively low frequencies of several hundred kHz, ultrasonic waves can also be used for flow velocity measurement, and since the SoS is greatly affected by the material composition, different materials and fluid mixtures will give different SoS. The ultrasonic sensor 130 operates at a frequency of 100 kHz to 2 MHz in one arrangement. The operating frequency range of the ultrasonic sensor 130 may be, for example, 100 kHz to 400 kHz for gaseous fluids and 400 kHz to 2 MHz for liquid fluids.

[0034] The FBG sensor 135 provides a fiber-based spectrometer that operates by sending light into an optical fiber, where the light is reflected back from the FBG. In theory, there can be one hundred or more FBGs on the same optical fiber at the same nominal center wavelength. At least in the case of a wavelength division multiplexing (WDM)-based FBG sensor, there are typically multiple FBGs in the FBG sensor 135. The reflected light travels back to the photodetector of the FBG sensor 135 and is compared with a wavelength reference artifact so that the FBG sensor 135 can determine the position of the center wavelength of the FBG. The wavelength information is converted to engineering units, for example, a wavelength shift of 1.3 picometers can correspond to 1 unit of microscopic strain. The actual translation is given by the sensitivity coefficient that is typically provided with the FBG sensor 135.

[0035] WDM is a technology originating from the telecommunications industry that uses multiple optical signals launched onto a single optical fiber by combining light of different wavelengths (i.e., colors) into the fiber. In this case of fiber optic sensing, the use of FBGs with different reflection wavelengths (Bragg wavelengths) in one optical fiber is described. The FBG sensor 135 can also utilize time division multiplexing (TDM), which uses the known speed of light in the optical fiber to determine which signal is reflected from which FBG along the optical fiber path. TDM can simplify interrogation of the same wavelength with time delays, so that a combination of WDM and TDM can also be used.

[0036] The FBG sensor 135 can provide high accuracy because the use of WDM (or WDM together with TDM) for the FBG sensor 135 enables sensing of specific wavelength reflections caused by deformation of its FBG due to humidity, temperature or strain / pressure changes. The FBG sensor 135 can operate at frequencies in the mid-infrared and near-infrared. For example, for an erbium-doped fiber amplifier (EDFA)-based FBG sensor 135, at 37THz to 400THz, it can operate at a power level of 20 to 24dB (indicating an amplification value of optical power), and at a frequency of 192THz to 196THz, it can operate at a power level corresponding to a wavelength of 1530nm to 1562nm.

[0037] Through the combination of at least two different measurement principles and wave types (e.g., electromagnetic waves provided by MMW sensor 145 and mechanical waves provided by ultrasonic sensor 130), the disclosed aspects recognize that complex multi-parameter based modeling can be achieved by utilizing ANN / AI learning implemented by ANN / AI block 127. In addition, FBG sensor 135 can perform fiber-based sensing of spectral signatures to refine the above results (and provide additional parametric information by collecting reflection and refraction characteristics at different wavelengths related to material properties), and process this data to act as a spectral mirror.

[0038] As described above, the computing system 120 generates a plurality of fluid parameters. An operator can enable / disable each of these fluid parameters using firmware configured to enable / disable functionality. With respect to constituent concentrations, this includes the concentrations of constituents known in the fluid mixture, as well as the concentrations of unexpected detected materials obtained by using a known database, such as by utilizing complex relative permittivity and density data. Figure 1A The density parameter shown may be achieved by the calculation system 120 using the molar mass divided by the molar volume, where the molar volume of an ideal gas is known to be 0.0227 m at 0° C. and 100 kPa according to the ideal gas formula. 3 / mol, 0.02479m at 25°C and 100kPa 3 / mol.

[0039] Figure 1A The water content shown may be achieved using data from a coated FBG sensor, wherein the coating is selected to make the FBG sensor sensitive to moisture. Fluid pressure and temperature may be provided by ultrasonic sensor 130. Volumetric flow may also be achieved using data from ultrasonic sensor 130, which measures path VoG to further calculate an average velocity across the cross-sectional area of ​​pipe 180, wherein the ultrasonic sensor 130 measured by its flow velocity may then be multiplied by the cross-sectional area of ​​pipe 180 to obtain the flow rate of the fluid mixture, the mass flow rate (which is calculated as the flow density multiplied by the flow volume), and the energy content achieved by the computing system 120 by treating the measured composition and its mole percentage as a concentration of C1 to C4 (butane) including natural gas which may be used to calculate the heating value (energy) value.

[0040] The calorific value (energy) calculation is a standard calculation when using a conventional gas chromatograph. Figure 1A The online chromatograph and fluid quality analyzer shown are implemented by the computing system 120 by understanding information for fluid quality inspection or main useful component detection. For example, the water or humidity level in natural gas should be as low as possible. Similarly, according to the regulations of different countries, and for carbon dioxide, methane, etc., and by identifying the components present in the fluid mixture to ensure the calorific value and comply with environmental regulations, the level of toxic gases such as H2S should be less than tens of ppm.

[0041] The sensor data diagnostics, calibration and fusion block 124 is used to put together the corresponding data from the corresponding sensors, which can be used to determine a model for pattern recognition. The ANN / AI learning block 127 can be used to establish a multi-sensor fusion model in vector space using the sensing signatures generated by the signature extraction block 123 based on the data provided by the MMW sensor 145, the ultrasonic sensor 130, and optionally the FBG sensor 135.

[0042] Figure 1B The FMPD system 100 ′ is now shown within the common housing 190 . Figure 1A The FMPD system 100 is shown mounted to a pipe along with mounting flanges 195 on respective ends thereof. The FMPD system 100' is shown to also include a function selector switch 185 and a display screen 196. The flange 195 includes holes shown to enable bolting to adjacent sections of pipe as with a conventional flow meter, wherein a piece of spool can be inserted between pipes having flanges 195 to be bolted thereto. Exemplary positions for controlling the orientation of the ultrasonic sensor 130, the MMW sensor 145, and the FBG sensor 135 are shown. The FMPD system 100' may also include other sensors.

[0043] Figure 2 An exemplary data processing flow chart 200 for determining multiple fluid parameters of a flowing fluid mixture is shown, wherein as described above, the FBG sensor is optional, which is now shown as a coated FBG sensor 135' that can implement WDM. The sensor data provided by the coated FBG sensor 135, the ultrasonic sensor 130, and the MMW sensor 145 are all coupled (through the ADC 150, then the pre-processing block 155, as shown in Figure 1) to the computing system now shown as 120', which implements the Bayesian classification analysis and the ANN / AI engine 127a.

[0044] The signature extraction block 123 of the computing system 120' performs signature extraction using the corresponding sensor data received from the three sensor types shown, which is then processed into vector space data 129, which is then classified by the material parameter classification and identification block 126, which is respectively shown as material classification block 126a and parameter identification block 126b, which provides multiple fluid parameters such as those described above. Figure 1A Identification of those shown.

[0045] Also shown is a data processing flow chart 200, including also Figure 1A 1 and 2. The material model and parameter block 125 for training samples or for loading a database is shown in FIG. 1. The timing control logic 220 is used to coordinate the timing of the corresponding measurements, the acquired measurement data and calculations and any control time series deemed necessary. The measurements typically come from ultrasonic sensors 130 (ultrasonic path measurements), from MMW sensors 145 (MMW velocity measurements) and from coated FBG sensors 135'.

[0046] Regarding the determination of the composition present in the fluid mixture in the case of a gas mixture, one of ordinary skill in the art can also utilize different correlation formulas developed by using the present disclosure to determine the components in the liquid mixture. The VoS in the gas is affected by the gas composition. The relationship for VoS can be expressed as follows:

[0047]

[0048] Among them, M r = composition density (g / mol), Z = gas compressibility factor, ρ = molar density, represents the partial derivative, C p / C v It is the ratio of the constant volume and constant pressure heat capacity of a gas. R = Universal Gas Constant = 8.31446261815324JK -1 mol -1. T is the temperature expressed in Kelvin. The molar density (Mr) can be expressed as:

[0049]

[0050] Since the main gas components in natural gas have different molar masses (molecular weights) and VoS, the signature from the ultrasonic sensor 130 can be used to identify and classify the corresponding components in the gas mixture, such as Figure 3 The velocity of a MMW (shown as VoE in the following formula) is affected by the dielectric constant and magnetic permeability of the medium in which the MMW travels.

[0051]

[0052] Where C0 is the VoE in vacuum (the same speed as the speed of light) = 3×10 8 m / s,∈ r = relative dielectric constant (RP) of the material, and μ r = its relative magnetic permeability.

[0053]

[0054] Likewise, the major gas components in natural gas have different relative permittivities (RP), which changes the velocity of the MMW. The frequency markers can be used to identify the molar masses of the respective components in a fluid mixture and thus their species, such as Figure 3 As shown, Figure 3 A table is provided with a column RP with RP values ​​for known components of natural gas. Different gas / liquid sources in the supply chain may have different percentages of these components, so that the numbers in the RP column show an indication that is not directly taken from a common source, but rather an estimate generated based on several factors. For example, the methane concentration of natural gas typically varies between 70% and 90%.

[0055] Example

[0056] The disclosed aspects are further illustrated by the following specific examples, which should not be construed as limiting the scope or content of the present disclosure in any way.

[0057] Figure 4An example of the disclosed data fusion arrangement is shown, where data from the MMW sensor 145, the ultrasonic sensor 130, and the FBG sensor 135 are all processed together by a computing system shown as 420, the computing system including a processor 121 and a memory 122, the computing system implementing a Bayesian classification block 421, an ANN engine 422, and a mathematical model block 423 (such as described above in the formula provided), as well as a statistical model (such as a probability distribution and a spectral component distribution). The data from each of the sensors 145, 130, and 135 is shown as being coupled to each of the Bayesian classification block 421, the ANN engine 422, and the mathematical model block 423. The respective outputs from the Bayesian classification block 421 output a vector of features extracted from the raw data, the ANN engine 422 outputs an abstract model, and the mathematical model 423 is an updated mathematical model including coefficients, each of which is shown as being stored in a database shown as 435.

[0058] Although various disclosed embodiments have been described above, it should be understood that they are presented by way of example only and not as limitations. Many changes may be made to the disclosed embodiments according to the disclosure herein without departing from the spirit or scope of the present disclosure. Therefore, the breadth and scope of the present disclosure should not be limited by any of the above-described embodiments. On the contrary, the scope of the present disclosure should be limited according to the following claims and their equivalents.

Claims

1. A fluid mixture parameter determination FMPD system (100) for analyzing a fluid mixture while moving, comprising: A computing system (120) comprising a processor (121) and associated memory (132), and at least one material model (125) comprising two or more model parameters for a plurality of material compositions stored in the memory; An ultrasonic sensor (130), a fiber Bragg grating (FBG) sensor and a millimeter wave (MMW) sensor (145), wherein the ultrasonic sensor, the fiber Bragg grating (FBG) sensor and the MMW sensor are each coupled to sense the fluid mixture and are each coupled to the computing system; the ultrasonic sensor is used to provide ultrasonic data to the computing system, including at least the velocity or volume flow rate of the fluid mixture and the speed of sound (VoS) through the fluid mixture, and the MMW sensor is used to provide MMW velocity data to the computing system, The FBG sensor is configured to perform fiber-based sensing to refine results including MMW velocity data from the MMW sensor and ultrasonic data from the ultrasonic sensor and provide additional parameter information by collecting reflection and refraction properties at different wavelengths related to material properties, wherein the computing system is configured to utilize the material model together with the ultrasonic data and the MMW velocity data to identify parameters including a plurality of components in the fluid mixture and concentrations of the plurality of components, and The computing system implements an artificial neural network (ANN) engine or an artificial intelligence (AI) engine configured to dynamically update the material model based on at least the ultrasonic data and the MMW velocity data measured online.

2. The FMPD system of claim 1, wherein the ultrasonic data comprises raw data used by the computing system to determine shape, amplitude, and phase.

3. The FMPD system of claim 1, wherein the computing system is further configured to determine the density and energy content of the fluid mixture.

4. The FMPD system of claim 1, wherein the computing system further implements a signal extraction module (123), a sensor data fusion module (124), and a material parameter classification and identification module (126).

5. A method comprising: A fluid mixture parameter determination FMPD system (100) is provided, the FMPD system comprising: a computing system (120), the computing system comprising a processor (121) and an associated memory (122) and at least one material model (125), the at least one material model comprising two or more model parameters for a plurality of material compositions stored in the memory, an ultrasonic sensor (130), a fiber Bragg grating (FBG) sensor and a millimeter wave (MMW) sensor (145), the ultrasonic sensor, the fiber Bragg grating (FBG) sensor and the MMW sensor are each coupled to the computing system and are each coupled to sense the fluid mixture as the fluid mixture moves; The ultrasonic sensor provides ultrasonic data to the computing system, including at least a velocity of the fluid mixture or a volumetric flow rate of the fluid mixture, and a velocity of sound (VoS) through the fluid mixture; The MMW sensor senses MMW speed data and provides the MMW speed data to the computing system, The FBG sensor is configured to perform fiber-based sensing to refine results including MMW velocity data from the MMW sensor and ultrasonic data from the ultrasonic sensor and provide additional parameter information by collecting reflection and refraction properties at different wavelengths related to material properties, and The computing system utilizes the material model along with the ultrasonic data and the MMW velocity data to identify parameters including a plurality of components in the fluid mixture and concentrations of the plurality of components, and Wherein, the computing system implements an artificial neural network (ANN) engine or an artificial intelligence (AI) engine configured to dynamically update the material model based on at least the ultrasonic data and the MMW velocity data measured online.

6. The method of claim 5, wherein the fluid mixture moves through a pipeline (180), and wherein coupling to sense the fluid mixture includes coupling the FMPD system to the pipeline to enable online determination of the plurality of components and concentrations of the plurality of components in the fluid mixture.

7. The method of claim 5, wherein the computing system further determines a density of the fluid mixture and an energy content of the fluid mixture.

8. The method of claim 5, wherein the fluid mixture comprises natural gas.

9. The method of claim 5, wherein the FMPD system further comprises a function selector configured to enable or disable individual ones of the parameters, and wherein the method further comprises a user of the FMPD system using the function selector to enable or disable individual ones of the parameters.

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