RU2793606C1 - System and method for determining energy content of flowing fluid medium in gaseous state - Google Patents

Abstract

FIELD: method and system for determining the energy content of a flowing fluid in a gaseous state.

SUBSTANCE: method for determining the energy content of a flowing fluid in a gaseous state, comprising the step of inferring, by means of an inference module (204), the energy content of a flowing fluid in a gaseous state from an inferred relationship between the energy content of a flowing fluid in a gaseous state, at least with one measurement taken on a flowing fluid in a liquid state, wherein at least one measurement contains the measured density and the energy content is the energy of the fluid during combustion. The method is carried out by means of a computer system (200) having a processor (210) and a storage device (220), wherein the storage device (220) has an inference module (204).

EFFECT: ability to accurately determine the energy content of the medium in the gaseous state online.

15 cl, 10 dwg

Description

The field of technology to which the invention belongs

The embodiments described below relate to energy content determinations, more specifically to inferential energy content determinations.

State of the art

Liquefied natural gas (hereinafter, "LNG") is becoming an increasingly important fuel because its high energy content makes it efficient for transportation. LNG is natural gas that is processed and liquefied by cooling to a low temperature. An example of typical transport conditions are at about -162°C and about 126.3 kilopascals (in absolute terms). The composition of LNG varies considerably depending on the source and based on the processing applied to the LNG. Typical compositions may include nitrogen, methane, ethane, propane, and higher order hydrocarbons (with four or more carbons in the chain). Due to compositional variation, it is difficult to know the energy content and combustion properties of various LNG mixtures upon delivery. LNG composition can significantly affect the value of LNG blends and the LNG content should be evaluated prior to purchase.

The current practice for determining the energy content of the delivered LNG is to measure the volume of the LNG and calculate the average density and average heating value from the analysis of the composition from the gas chromatograph. The general equation used is Equation (1):

(1)

(1)

In equation (1), V _LNG is the volume of LNG measured in the tanks of an LNG tanker, δ _LNG is the density of LNG calculated from chromatographic analysis and LNG temperature, and H _LNG is the mass average gross calorific value (GCV) of LNG calculated from chromatographic analysis of LNG.

As can be seen, existing systems require the use of chromatographs in order to determine the relative composition of gases. Gas chromatographs take a long time to make determinations because the sampling and analysis process is slow. Additionally, chromatography is expensive and cannot be performed in real time. During the time it takes to analyze a sample, the composition of the flowing LNG can change dramatically, making chromatographic determinations impractical for purposes of determining the energy content of the LNG being evaluated. Typical LNG and other liquid line measurements include simpler parameters such as density, viscosity, pressure, and speed of sound (hereinafter referred to as "SOS"). The measurement of these parameters is more practical online. However, these measurements do not represent direct measurements of energy content. Inferential definitions are inferential definitions in which there is no direct relationship between the measured parameter and the calculated variable with respect to the measured parameters. If typical fluid measurements taken under linear conditions can be applied to inferred relationships to infer energy content, the resulting inferences can benefit from higher sampling rates and novelty. The process should also have the advantage of eliminating costly sampling and chromatography procedures.

Accordingly, there is a need for systems that use inferential relationships with typical LNG measurements in order to determine live energy content values.

The essence of the invention

Disclosed are embodiments of methods for inferring the energy content of a flowing fluid in a gaseous state. The method may be carried out by a computer system (200) having a processor (210) and a storage device (220), wherein the storage device (220) has an inference module (204), wherein the method comprises an inference, by means of an inference module (204) , the inferred energy content of the flowing fluid in the gaseous state from an inferred relationship between the inferred energy content of the flowing fluid in the gaseous state with at least one measurement made on the flowing fluid in the liquid state.

Disclosed are embodiments of equipment for inferring the energy content of a flowing fluid in a gaseous state. The equipment has a computer system (200), wherein the computer system (200) has a processor (210) and a storage device (220), wherein the storage device (220) has an inference module (204), wherein the inference module (204) is configured to infer the inferred energy content of the flowing fluid in the gaseous state from the inferred relationship between the inferred energy content of the flowing fluid in the gaseous state with at least one measurement made on the flowing fluid in the liquid state.

Aspects

According to an aspect, a method is disclosed for inferring the energy content of a flowing fluid in a gaseous state. The method may be carried out by a computer system (200) having a processor (210) and a storage device (220), wherein the storage device (220) has an inference module (204), wherein the method comprises an inference, by means of an inference module (204) , the inferred energy content of the flowing fluid in the gaseous state from an inferred relationship between the inferred energy content of the flowing fluid in the gaseous state with at least one measurement made on the flowing fluid in the liquid state.

Preferably, at least one measurement contains the measured density.

Preferably, at least one measurement further comprises one or more of the measured viscosity and the measured speed of sound.

Preferably, the inferred relationship is the sum of the terms, with each term having one or more of one of at least one dimension and one higher order value of one of at least one dimension.

Preferably, each member has a coefficient that corresponds to the member.

Preferably, each coefficient is temperature dependent, wherein the temperature dependence of each coefficient has at least one member-specific constant as a coefficient.

Preferably, the relationship has at least five terms, at least five terms include a shift term, a measured density term having a measured density, a higher order density term having a higher order value of the measured density, at least one of a measured density term viscosity and a sound speed term, respectively, having one or more of a measured viscosity and a measured sound speed, and at least one of a higher order viscosity term or a higher order sound speed term, respectively, having one or more of a higher order value of one or more of the measured viscosity and the measured speed of sound.

Preferably, the flow fluid is a natural gas mixture.

Preferably, the energy content is one of methane number, lower flammability limit, Wobbe index, gross calorific value, and net calorific value.

Preferably, the computer system (200) is the vibration sensor (102) meter electronics (110), wherein the vibration sensor (102) measures one or more of at least one measurement.

According to an aspect, equipment is disclosed for inferring the energy content of a flowing fluid in a gaseous state. The equipment has a computer system (200), wherein the computer system (200) has a processor (210) and a storage device (220), wherein the storage device (220) has an inference module (204), wherein the inference module (204) is configured to infer the inferred energy content of the flowing fluid in the gaseous state from the inferred relationship between the inferred energy content of the flowing fluid in the gaseous state with at least one measurement made on the flowing fluid in the liquid state.

Preferably, at least one measurement contains the measured density.

Preferably, at least one measurement further comprises one or more of the measured viscosity and the measured speed of sound.

Preferably, the inferred relationship is the sum of the terms, with each term having one or more of one of at least one dimension and one higher order value of one of at least one dimension.

Preferably, each member has a factor that corresponds to the member.

Preferably, each coefficient is temperature dependent, wherein the temperature dependence of each coefficient has at least one member-specific constant as a coefficient.

Preferably, the relationship has at least five terms, wherein the at least five terms comprise a shift term, a measured density term having a measured density, a higher order density term having a higher order value of the measured density, at least one of a measured viscosity term and a sound velocity term, respectively, having one or more of the measured viscosity and a measured sound velocity, and at least one of a higher order viscosity term or a higher order sound velocity term, respectively, having one or more of the higher order value of the order of one or more of the measured viscosity and the measured speed of sound.

Preferably, the flow fluid is a natural gas mixture.

Preferably, the energy content is one of methane number, lower flammability limit, Wobbe index, gross calorific value, and net calorific value.

Preferably, the equipment is a vibration sensor (102), while the computer system (200) is the electronic circuits (110) of the vibration sensor (102) meter, and the vibration sensor (102) measures one or more of at least one measurement .

Brief description of the drawings

Identical reference numbers represent the same element throughout the drawings. It should be understood that the drawings need not be drawn to scale.

Fig. 1 shows a block diagram of an embodiment of a flow fluid metering system.

Fig. 2 shows a block diagram of an embodiment of computer system 200.

Fig. 3 shows a flowchart of an embodiment of a method 300 for energy content inference.

Fig. 4 shows a flowchart of an embodiment of a method 400 for energy content inference.

Fig. 5 shows a flowchart of an embodiment of a method 500 for energy content inference.

Fig. 6 shows a plot 600 of the correspondence between measured Wobbe index values and inferred Wobbe index values inferred from an inferred relationship embodiment.

Fig. 7 shows a plot 700 of the correspondence between measured methane numbers and inferred methane numbers inferred from an inferred relationship embodiment.

Fig. 8 shows a plot 800 of the correspondence between the measured lower flammable limit and the inferred lower flammable limit inferred from the inferred relationship embodiment.

Fig. 9 shows a plot 900 of the correspondence between the measured gross calorific value and the inferred gross calorific value inferred from the inferred relationship embodiment.

Fig. 10 shows a plot 1000 of the correspondence between the measured net calorific value and the inferred net calorific value inferred from the inferred relationship embodiment.

Detailed description of the invention

Fig. 1-10 and the description below illustrate specific examples in order to teach those skilled in the art on how to implement and use the optimal mode of energy content inference embodiments. For the purposes of studying the principles of the invention, some conventional aspects are simplified or omitted. Those skilled in the art will be aware of variations of these examples that fall within the scope of the present description. Those skilled in the art will appreciate that the features described below can be combined in various ways to form multiple variations of energy content inferences. As a result, the embodiments described below are not limited to the specific examples described below, but only by the claims and their equivalents.

By isolating the type of fluid into specific classes, such as natural gas mixtures, it can be seen that simple relationships between typically measured values in fluid flow arrangements can be used to infer the energy content of fluids. This is entirely true if the measurements and the inferred relationship are determined based on the liquid state values in order to infer the energy content in the gaseous state. When the terms "infer" or "inference" are used in the verb form, it should be understood that this means the need to determine using inferential associations, for example, using inferential relationships. This logical conclusion can be carried out without any direct measurements of thermal indicators, for example, thermal conductivity, heat capacity and thermal diffusion coefficient. Additionally, the inference may be performed without considering other conventional factors for determining energy content, such as permeability, laminar drag, turbulent drag, and refractive index. In addition, the inference can be performed without artificially generating temperature and/or pressure drops in the metering equipment beyond those temperature and pressure drops associated with typical flowmeter-fluid interactions.

Because these relationships are relatively simple for particular classes of gases, such as natural gas mixtures, inferred relationships can be represented as linear combinations of simple measurements in the liquid state with associated coefficients. For example, the inferred relationship may be so simple that it simply takes into account the measurement(s) of the fluid in the liquid state, possibly under linear conditions. The relationship may include an appropriate factor to measure the fluid in the liquid state. In an embodiment, the corresponding coefficient may have a temperature-dependent relationship such that the corresponding coefficient varies depending on the measured fluid temperature in the liquid state. In an embodiment, each of the measured fluid state values (other than potentially temperature) that are used in an inferential relationship may have a different corresponding temperature dependent coefficient. It should be noted that despite the mixture being referred to as "natural gas", natural gas mixtures in liquid (ie LNG) and gaseous states are contemplated when using the term "natural gas". It should be noted that the description of the invention is not limited to natural gas mixtures and can be applied to other classes of fluid with energy content, which can be in liquid and gaseous states.

The inferred relationship may further have a shift term (A) that serves as a reference value relationship for the energy content. The shift term can also be temperature dependent (K ₁ (T)). In an embodiment, one of the measured values of the fluid in the liquid state is the measured density of the fluid in the liquid state. The measured density may be an element of the density term (B) of the inferred relationship. The density term (B) may be the product of the measured density and the corresponding factor for the measured density. In an embodiment, the relationship may be the sum of a shift term and a density term.

In another embodiment, the measured values of the fluid in the liquid state may further include the measured sound velocity of the fluid in the liquid state. The relationship may additionally take into account the measured speed of sound. For example, the relationship may additionally have a speed of sound term that includes the measured speed of sound. In this embodiment, the speed of sound term may be the measured speed of sound multiplied by an appropriate factor that corresponds to the speed of sound. In an embodiment, the relationship may be the sum of a shear term, a density term, and a sound speed term. In yet other embodiments, the speed of sound may be replaced by a measurement of viscosity. For example, the relationship may have a viscosity term that includes the measured viscosity. In this embodiment, the viscosity term may be the measured viscosity multiplied by an appropriate factor that corresponds to the measured viscosity. In an embodiment, the relationship may be the sum of a shear term, a density term, and a viscosity term.

Relationships that include a single measured fluid in the liquid state that is not a measured temperature may take the form of Equation (2)

(2)

(2)

In equation (2), IEC _Gas is the inferred energy content of the fluid in gaseous form. A is a shift member. B is the density term as shown here, but it should be appreciated that other measured terms may be used instead in equation (2).

In all embodiments, the shift term (A) may be expressed as a constant, or may be expressed as a temperature dependent quantity (K ₁ (T)), possibly having a simple relationship with temperature, as shown in equation (3):

(3)

(3)

The density term (B) can be expressed as the product of the measured density (ρ _liquid ) of the fluid in the liquid state with a factor that corresponds to the measured density (K ₂ ) as shown in equation (4):

(4)

(4)

In an embodiment, the coefficient that corresponds to the measured density (K ₂ ) may be a temperature dependent coefficient (K ₂ (T)) such that equation (4) becomes equation (5).

(5)

(5)

In an embodiment, equation (2) may take the form of equation (6)

(6)

(6)

It should be appreciated that embodiments in which some or all of the coefficients and the shift term are constants and do not vary with temperature.

In an embodiment where more than one measured fluid state quantity in the liquid state (more than one measured quantity not including measured member temperature but having coefficients potentially dependent on temperature) is used in an inferential relationship, the inferred relationship may take form of equation (7):

(7)

(7)

The shift term (A) and the density term (B) may be as expressed in Equations (3) to (6). In an embodiment where the sound speed of the fluid in the liquid state is one of more than one measured quantity used in the inferential relationship, the relationship may have a sound speed term (C) as shown in equation (7).

The sound speed term (C) may be expressed as the product of the measured sound speed (SOS _liquid ) of the fluid in the liquid state by a factor that corresponds to the measured sound speed (K ₃ ) as shown in equation (8):

(8)

(8)

In an embodiment, the coefficient that corresponds to the measured sound velocity (K ₃ ) may be a temperature dependent coefficient (K ₃ (T)) such that equation (8) becomes equation (9).

(9)

(9)

In an embodiment, the relationship expressed in equation (7) may be expressed as equation (10).

(10)

(10)

In various embodiments in which there is more than one fluid state measurement (more than one measurement not including measured member temperature but having coefficients potentially dependent on temperature), fluid viscosity measurement may be used. instead of or in addition to the speed of sound. In this embodiment, the viscosity term (D) may be used in addition to or instead of the sound speed term (C).

The viscosity term (D) can be expressed as the product of the measured viscosity (η _liquid ) of the fluid in the liquid state by a factor that corresponds to the measured viscosity (K ₄ ) as shown in equation (11):

(11)

(eleven)

In an embodiment, the coefficient that corresponds to the measured viscosity (K ₄ ) may be a temperature dependent coefficient (K ₄ (T)) such that equation (11) becomes equation (12).

(12)

(12)

In an embodiment, the inferred relationship may be a sum that includes the viscosity term (D), with the density term (B) and the shear term (A), but not with the sound velocity term (C), as shown in equation (13) :

(13)

(13)

In an embodiment, the relationship of equation (13) may be expressed as equation (14).

(14)

(14)

In yet another embodiment, all of the shear term (A), the density term (B), the sound velocity term (C), and the viscosity term (D) may be considered in an inferential relationship. For example, the inferred relationship may be the sum of the shear term (A), the density term (B), the speed of sound term (C), and the viscosity term (D), as shown in equation (15).

(15)

(15)

In an embodiment, the relationship of equation (13) may be expressed as equation (16).

(16)

(16)

The inferred relationship can additionally take into account any number of terms (hereinafter in this document, "higher order terms") with exponentials squared or higher order of the measured parameters used (hereinafter in this document, "higher order measurements"), for example, with exponentials squared or higher order of one or more of the measured density of the fluid in the liquid state, the measured speed of sound of the fluid in the liquid state, and the viscosity of the fluid in the liquid state. The inferred relationship may have corresponding coefficients for each of the higher order dimensions. The corresponding higher order measurement factors may have temperature dependencies. Higher order dimensions can be represented in inferential relationships in higher order terms. In various embodiments, the higher order terms may be the product of each higher order dimension and each corresponding coefficient. One or more higher order terms may be included in inferred relationships as additional sums, for example, additional sums of higher order terms to be added to the right side of any of equations (2), (6), (7), (10) , (13), (14), (15) and (16).

In an embodiment, the inferred relationship may be quadratic in certain terms and may take the form of Equation (17):

(17)

(17)

In equation, (17), K ₅ (T) and K ₆ (T) are temperature dependent coefficients for the measurement values of density squared and viscosity squared, respectively. An alternative embodiment is contemplated in which the coefficients are constants that do not vary with temperature (ie, K ₁ -K ₆ are constants). This provides quadratic relationships between each of density and viscosity with the inferred energy content of the gas.

In an embodiment, each of the members of the inferred relationship may have only one of a measured value and a higher order measured value.

Temperature dependence of one or more temperature-dependent coefficients (for example, K ₁ (T), K ₂ (T), K ₃ (T), K ₄ (T), K ₅ (T) and/or K ₆ (T)) can be defined by any number of relationships. For example, the relationship between coefficient and temperature may be linear, with an embodiment of this being shown in Equation (18):

(18)

(18)

In equation (18), G and H are constants (hereinafter in this document, "coefficient constants") that can be determined by means of analysis, such as regression, for several different gas mixtures in different temperature ranges. Each term may have a temperature-dependent coefficient, and each temperature-dependent coefficient may have at least one member-specific coefficient constant (e.g., G and/or H may be coefficient-specific member constants for approximately "x -th" term in equation (19)). The subscript "x" should simply mean that the coefficient relationship described in equation (18) is common to any corresponding measurement value (or higher order measurement value, such as measured density squared) in an inferential relationship. "Relationship elements" may include one or more coefficients and constants in the form of coefficients. For purposes of describing the invention, if an inferential relationship structure is defined, a structure such as the form of one or more equations (2) to (19), the inferred relationship may be distinguished by this structure and relationship elements.

In another embodiment, the temperature dependence of one or more temperature dependent coefficients (e.g., K ₁ (T), K ₂ (T), K ₃ (T), K ₄ (T), K ₅ (T) and/or K ₆ (T)) may be determined by a quadratic relationship with temperature, an embodiment of this being shown in Equation (19):

(19)

(19)

In equation (19), G, H, and I may be constants, which may be determined by means of analysis, such as regression, for several different gas mixtures in different temperature ranges. On the other hand, the subscript "x" should simply indicate that the coefficient relationship described in equation (19) is common to any corresponding dimension value (or higher order dimension value) in the inferential relationship. Each of the temperature-dependent coefficients (for example, K ₁ (T), K ₂ (T), K ₃ (T), K ₄ (T), K ₅ (T) and/or K ₆ (T)) may have different the values of one or more of G, H and I and/or may have different order polynomials in temperature for each of the coefficients such that more or fewer coefficients are used. One or more of the relationships expressed in equations (2)-(19) may be used to infer the values of one or more of MN, LFL, WI, GHV, and NHV from typical measurements taken under linear conditions.

Examples of these implementations are shown in FIG. 6-10 and in their respective descriptions.

It should be appreciated that embodiments in which some or all of the coefficients and the shift term are constants and do not vary with temperature.

Fig. 1 shows a block diagram of an embodiment of a flow fluid metering system. System 100 has a vibration sensor 102, an optional sound velocity sensor 106, and an optional additional vibration sensor 199. It will be appreciated that any system with vibration sensor 102 may be used, such as a Coriolis flow meter, fork density meter, fork viscometer, and the like. The same applies to the optional vibration sensor 106 . In various embodiments, multiple vibration sensors 102 of identical or different types may be used in series to determine the measurements to be used in inferential energy content determinations.

The vibration sensor 102 and/or 199 may be used to provide typical measurements of the fluid flow and/or fluid flow of the fluid that interacts with the vibration sensor. Typical measurements provided by vibration sensors 102 and/or 199 may include, for example, one or more of density, viscosity, speed of sound, mass flow, and volume flow of a fluid in the liquid state. Vibration sensor 102 and optional additional vibration sensor 199 may be different types of vibration sensors such that they are constructed differently and/or may provide different measurements relative to each other. For example, the vibration sensor 102 may be a fork viscosity meter, and the optional additional vibration sensor 199 may be a Coriolis flow sensor. This is merely exemplary and all variations of potential flow sensors 102 and/or combinations of flow sensors 102 and optional additional flow sensors 199 are assumed.

The vibration sensor 102 and/or 199 may be mounted in a pipe or conduit, tank, container, or other fluid vessel. The vibration sensor 102 and/or 199 may also be mounted in a manifold or the like to direct fluid flow. However, other mounting arrangements are contemplated and are within the scope of the description and claims.

In an embodiment, the vibration sensor 102 and/or 199 may be a fork meter, such as a fork viscosity meter or a fork density meter. Vibration sensor 102 and/or 199 may have meter electronics 110, control signal generator 103, first prong 104a, second prong 104b, response sensor 105, temperature sensor 108, and communication line 26. Vibration sensor 102 is operable to provide measurements of fluids. Vibration sensor 102 may provide fluid measurements including, for example, one or more of fluid density (ρ), fluid temperature (T), fluid viscosity (η), mass flow, volume flow, and pressure (P) for fluid media, including flowing or non-flowing fluids. This list is not exhaustive, and the vibration sensor 102 and/or 199 may measure or determine other characteristics of the fluid.

The meter electronics 110 is a processing circuit that processes raw signal data for measurement and/or processing by software modules. The meter electronics 110 may be an embodiment of the computer 200 shown in FIG. 2. The meter electronics 110 controls the operation of the control signal generator 103 and the response sensor 105 of the vibration sensor 102, and can provide electrical power to the control signal generator 103 and the response sensor 105. For example, the meter electronics 110 may generate a drive signal and provide the generated drive signal to the control signal generator 103 to generate vibrations in the first tooth 104a.

The first tooth 104a is a submerged element of the vibration sensor 102. The generated excitation signal can control the amplitude and frequency of vibrations of the first tooth 104a. The generated excitation signal can also control the duration of the vibrations and/or the timing of the vibrations. It should be noted that meter electronics 110 may represent multiple components and products that are used together but may be sold separately. For example, meter electronics 110 may include meter electronics and other operatively connected electronics, such as a transmitter or other device the use of which requires the meter and its electronics.

The control signal generator 103 is an element that excites movements. The first tooth 104a is an element that vibrates and interacts with the fluid. The control signal generator 103 may receive drive signals from the meter electronics 110 to vibrate the first tooth 104a. The second tooth 104b is another submerged element that has a net vibration, possibly excited by the vibration of the first tooth 104a. The second tooth 104b is connected to a response sensor that measures the vibrational response of the second tooth 104b such that the relationship between the vibrational response of the second tooth 104b and the control signal generator signal applied to the control signal generator 103 that drives the first tooth 104a represents fluid properties. These vibrations may be excited to enable measurements of the flowing fluid and/or fluid flow by the meter electronics 110. Temperature sensor 108 is a device that measures temperature. Fluid and/or fluid flow measurements may have temperature dependencies such that temperature sensor 108 may provide temperature data to meter electronics 110 for use in measurements.

The meter electronics 110 may receive a vibration signal or signals from a response sensor 105 that detects movement and/or vibrations of the second tooth 104b. In an embodiment, the meter electronics 110 may drive the vibration element in phase lock such that the command signal provided to the control signal generator 103 and the response signal received from the response sensor 105 are in phase lock. The meter electronics 110, for example, may process the vibration signal or signals to generate a density (ρ) measurement. The meter electronics 110 processes the vibration signal or signals received from the response sensor 105 to determine the frequency of the signal or signals. Additionally or in addition, the meter electronics 110 process the vibration signal or signals to determine other characteristics of the fluid, such as viscosity (η). In alternative embodiments, the meter electronics 110, for example, may also determine the phase difference between the upstream and downstream signals, which may be processed to determine the flow rate of the fluid. It may be appreciated that the phase difference is typically measured or expressed in spatial units such as degrees or radians, although any suitable unit such as time units may be used. If time units are used, then the phase difference may be referred to by those skilled in the art as the "time delay" between the vibration signal and the excitation signal. Other characteristics of rapidity of vibration response and/or measurement of fluids are contemplated and are within the scope of the description and claims.

The electronic circuits 110 of the meter can additionally be connected to the communication line 26 . The meter electronics 110 may transmit the vibration signal over the communication line 26 . The meter electronics 110 may also process the received vibration signal to generate a measurement value or values and may transmit the measurement value or values over the communication line 26 . In addition, the meter electronics 110 can receive information via the communication line 26 . For example, meter electronics 110 may receive commands, updates, operating values or operating value changes and/or software updates, or switch communication link 26 . In various embodiments, communication line 26 may be an embodiment or operably connected to functional communication module 240.

The vibration sensor 102 and/or 199 can provide a drive signal to the control signal generator 103 using a closed loop circuit. The excitation signal is typically based on the received vibration signal. The closed loop circuit may modify or include the vibration signal or vibration signal parameters in the drive signal. For example, the excitation signal may be an amplified, modulated, or otherwise modified version of the received vibration signal. The received vibration signal may therefore contain feedback that allows the closed loop circuit to reach the target frequency or phase difference. Using feedback, the closed loop circuit incrementally changes the drive frequency and tracks the vibration signal until the target phase is reached such that the drive frequency and the vibration signal are phase locked at or near the target phase.

Fluid properties such as viscosity (η) and density (ρ) of the fluid can be determined from frequencies at which the phase difference between the drive signal and the vibration signal is 135° and 45°. These required phase differences, referred to as the first non-resonant phase difference ϕ1 and the second non-resonant phase difference ϕ2, may correspond to half power or frequencies of 3 dB. The first non-resonant frequency ω1 is set as the frequency at which the first non-resonant phase difference ϕ1 is 135°. The second non-resonant frequency ω2 is set as the frequency at which the second non-resonant phase difference ϕ2 is 45°. Density measurements (ρ) made at the second non-resonant frequency ω2 may be independent of the viscosity (η) of the fluid. Accordingly, density (ρ) measurements made when the second non-resonant phase difference ϕ2 is 45° can be more accurate than density (ρ) measurements made at other phase differences.

In some embodiments, the vibration sensor 102 may only determine one of density (ρ) and viscosity (η), with another implementation determining the other of density (ρ) and viscosity (η), with the other implementation possibly being a different vibrometer.

Various embodiments of vibration sensor 102 are contemplated, and the embodiment shown in FIG. 1 is merely for exemplary purposes. Any vibration sensor 102 may be used, such as the fork meter described or a Coriolis flow sensor.

The optional sound velocity sensor 106 is a sensor that detects the sound velocity of the fluid. The optional sound velocity sensor 106 may determine the sound velocity of the fluid in the liquid state in order to determine the energy content of the fluid in the gaseous state. The optional sound velocity sensor 106 can transmit sound, using a sound transmitter, through the liquid fluid to be measured and receive, using the acoustic sensor, a response. The speed of sound can then be determined based on the transition time and the distance between the sound transmitter and the acoustic sensor. They are merely examples, and other means of measuring the speed of sound via the optional sound speed sensor 106 are contemplated.

Although not illustrated, one or more vibration sensors 102 and/or 199 may be a Coriolis flow sensor. Coriolis flow sensors can detect phase differences in measured vibrations due to Coriolis forces to determine the mass flow rate and/or density of a fluid, possibly a fluid in a liquid state and/or a fluid in a gaseous state. In an embodiment, neither vibration sensor 102 and optional additional vibration sensor 199 are fork meters (so vibration sensor 102 shown in FIG. 1 is different from vibration sensor 102 of this embodiment). In another embodiment, the vibration sensor 102 may be a gas density meter that is based on vibration. The ways in which vibration sensor(s) 102 and/or 199 and optional sound velocity sensors 106 measure and determine measured values are conventional in the art, and further disclosure is omitted for brevity.

The computer system, such as meter electronics 110 of vibration sensor 102, may be configured to use one or more typical measurements of fluid flow and/or fluid flow to infer an energy content index value for a gaseous fluid, for example, with using any of the relationships expressed in equations (2)-(19) and other examples studied in this description of the invention.

Examples of typical energy content ratings include methane number (hereinafter referred to as "MN"), lower flammable limit (hereinafter referred to as "LFL"), Wobbe index (hereinafter referred to as "WI"), gross calorific value (hereinafter in this document, "GHV") and net calorific value (hereinafter in this document, "NHV"). In the embodiments disclosed in this detailed description, the inferred energy content may be one or more of MN, LFL, WI, GHV, and NHV.

MN is a measurement of energy content, which may represent the impact potential of a fluid upon ignition. It describes the probability that the fuel should ignite uncontrollably. An embodiment of the relationship for finding MN is shown in Equation (20):

(20)

(20)

является атомным соотношением водорода и углерода (например,

для метана составляет четыре для метана, который имеет четыре водорода и один углерод). Чтобы определять его непосредственно, следует знать состав текучей смеси, то, что сложно определять при линейных условиях.In equation (19), MN is the methane number, and

is the atomic ratio of hydrogen and carbon (for example,

for methane is four for methane, which has four hydrogens and one carbon). To determine it directly, one should know the composition of the fluid mixture, something that is difficult to determine under linear conditions.

LFL is an energy content measurement that represents the minimum composition of gas mixed with air in which combustion must occur. An embodiment of the relationship for finding the LFL is shown in Equation (21):

(21)

(21)

In equation (21), LFL is the lower flammable limit, i is an index referring to each component of the mixture, x _i is the relative proportion of component i, and LFL _i is the lower flammable limit of component i. Methods using this relationship are limited by the need to know the composition. Composition can be difficult to determine under linear conditions.

WI is an energy indicator that represents the interchangeability of fuel gases. WI is the calorific value relative to the root of the specific gravity. Equation (22) shows an embodiment of the relationship for determining WI:

(22)

(22)

In equation (22), WI is the Wobbe index, C _V is the heating value, and SG is the specific gravity of the fluid. On the other hand, traditional measurements to determine the calorific value require knowledge of the relative composition of the mixture and require determination of the composition. Composition determinations may not be practical in determining or inferring live measurements under linear conditions.

GHV and NHV are calorific values often referred to as "calorific values". The difference between GHV and NHV is that NHV is reduced by the amount of heat that should be produced by the condensation of any water vapor in the mixture. An embodiment of the method for determining GHV is shown in equation (23):

(23)

(23)

In equation (23), GHV _V is the gross calorific value (in units of volume), %CO ₂ is the carbon dioxide composition of the mixture by volume, and %N ₂ is the nitrogen composition of the mixture by volume. Equation (23) is the relationship of the AGA5 equation in volume units. It should be noted that only terms for carbon dioxide and nitrogen are shown, but more elements exist in the equation for other substances, which are omitted for brevity. In some systems, equation (23) results in the heating value in BTU per cubic foot at 14.73 psi and 60°F.

A unit of mass equivalent to the AGA5 equation may also be used. An embodiment of an equivalent mass unit is shown in Equation (23A):

(23A)

(23A)

In equation (23A), GHV _M is the gross calorific value (in units of mass), M _C is the composition of carbon dioxide by mass, M _N is the composition of nitrogen by mass, and SG is the specific gravity.

To obtain NHV, one can use the resulting GHV from equations (23) or (23A) and subtract from it the heat of condensation of any water vapor in the mixture. On the other hand, what is meant must require a definition of the composition. Composition determinations may not be practical in determining or inferring live measurements under linear conditions.

In an embodiment where the inferred relationship is independent of the sound velocity of the fluid in the liquid state, one vibrometer 102, possibly a fork viscosity meter (hereinafter, "FVM"), may be used to determine the inferred energy content fluid in the gaseous state based on measurements made for the fluid in the liquid state. The measurements taken by the FVM may include a measured density and a measured viscosity. These measured values given for the fluid in the liquid state can be used to infer the energy content of the fluid in the gaseous state. In an embodiment that uses an FVM (eg, vibration sensor 102), a separate Coriolis flow sensor (eg, optional additional vibration sensor 199) can still be used to determine the mass flow rate of fluid in the liquid state. When both mass flow and energy content are determined by any of the systems disclosed in this specification, the systems can further derive from the mass flow and energy content of the fluid in the liquid state the energy velocity of the fluid flow such that the fluid flow in the liquid state is measured at the energy that the flowing fluid can provide in the gaseous state per unit time.

In another embodiment, the inferred relationship does indeed depend on the measured sound velocity of the fluid in the liquid state. In this embodiment, an optional sound velocity sensor 106 may be used to determine the sound velocity of a fluid in the liquid state. In an embodiment that uses the sound velocity of the fluid in the liquid state and uses the density of the fluid in the liquid state, measurements of the sound velocity of the fluid in the liquid state, as determined by the optional sound velocity sensor 106, may be transmitted to another computer, possibly electronic vibration sensor circuitry 102 and/or 199 to infer the energy content of the fluid in the gaseous state on another computer. In this embodiment, one or more of the density and viscosity of the fluid in the liquid state may be determined by the vibration sensor 102 and/or 199 and used with the transmitted sound velocity measurement of the fluid in the liquid state in order to infer the energy content of the fluid in the gaseous condition.

Additional embodiments are envisaged in which multiple vibration sensors 102 and/or 199 are used to measure one or more of the mass flow rate, density, and viscosity of the fluid in the liquid state, and/or an optional sound velocity sensor 106 is used to measure the speed of sound of a fluid in a liquid state. All combinations of potential hardware and software arrangements based on the types of sensors disclosed and measurements potentially used in energy content inference are contemplated by this specification.

Fig. 2 shows a block diagram of an embodiment of computer system 200. In an embodiment, computer system 200 may be meter electronics, such as meter electronics 110. In various embodiments, computer system 200 may be composed of ASICs or may have discrete processing and storage elements, the processing elements serving to process instructions and store data from/to the storage elements. Computer system 200 may be an isolated physical system, a virtual machine, and/or may be installed in a cloud computing environment. Computer system 200 may be configured to perform any of the method steps presented in this specification and may perform all functions associated with the disclosed modules.

The computer system may have a processor 210, a storage device 220, an interface 230, and a functional link module 240. The storage device 220 may store and/or may have integrated circuits representing, for example, an analysis module 202, an inference module 204, and a measurement module 206. In various embodiments, computer system 200 may have other computer elements integrated into the claimed elements or in addition to or in communication with the claimed computer elements, such as buses, other communication protocols, and the like.

Processor 210 is a data processing element. Processor 210 can be any element used for processing, such as a CPU, an ASIC, another integrated circuit, an analog controller, a graphics processor, a field programmable gate array, any combination of the above, or other general processing elements, and the like. Processor 210 may include a cache to store processing data. The processor 210 may benefit from the methods in this detailed description as the methods can increase the resolution of calculations and reduce the error of these calculations using the presented inventive structures.

The storage device 220 is an electronic data storage device. The storage device 220 may be any non-volatile storage medium and may include one, part, or all of a hard disk drive, a semiconductor drive, a volatile storage device, integrated circuits, a field programmable gate array, a random access memory, a read only memory, a dynamic random access a memory device, an erasable programmable read only memory, an electrically erasable programmable read only memory, a cache memory, and the like. The processor 210 may execute instructions from and use the data stored in the storage device 220.

The computer system 200 may be configured to store any data that is used by the analysis module 202, the inference module 204, and/or the measurements module 206, and may store for any amount of time statistical data representing any parameter received or used by the module. 202 analysis, inference module 204, and/or measurement module 206, in memory 220, possibly with timestamps representing when the data was acquired or determined. Computer system 200 may also store any data that represents definitions of any intermediate elements in memory 220. Although analysis module 202, inference module 204, and/or measurement module 206 are displayed as three separate and discrete modules, the description of the invention assumes any number ( even one or three as indicated) and a plurality of modules working together to implement the methods expressed in the detailed description.

The analysis module 202 is a software module that determines an inferential relationship between the energy content of the fluid in the gaseous state and the fluid parameters measured while the fluid is in the liquid state. Analysis module 202 may use any of the methods and equations disclosed in this detailed description to determine an inferential relationship, such as the methods disclosed in the description of FIG. 1 and in equations (2)-(19). The analysis module may determine, using an estimation procedure such as regression or a machine learning algorithm, an inferential relationship using existing data. For example, the analysis may be performed for various relationships that include various measured parameters of the fluid in the liquid state and provide convergence of the resulting inferred energy content to a known, measured energy content of the fluid in the gaseous state. For example, analysis module 202 may receive data representing one or more of a measured fluid density in a liquid state, a measured sound velocity of a fluid in a liquid state, and/or a measured viscosity of a fluid in a liquid state, and corresponding measured energy content values of the identical fluid in gaseous state, and determine the values of the inferential relationship elements in the inferred relationship that relate the measured fluid parameters in the liquid state to the measured energy content values of the identical fluid in the gaseous state. In various embodiments, the inferred relationship should have elements that are temperature dependent such that the determination of the inferred relationship, by the analysis module 202, also requires receiving measured fluid temperature values in the liquid state, the measured temperature possibly being obtained simultaneously or almost simultaneously with other measured values. The elements of the inferred relationship may include measured parameters, higher power levels of the measured parameters, coefficients (possibly corresponding coefficients that correspond to one or more measured parameters or higher power levels of the measured parameters), temperature dependences of various coefficients, and the like. The inferential relationship may be described by one or more of equations (2)-(19), and the analysis module may use one or more of the relationships expressed in equations (2)-(19) to determine the inferential relationship. The analysis module 202 may further use equations with higher order terms of the measurement values (eg, quadratic terms) to determine an inferential relationship, as discussed in this detailed description.

Analysis module 202 may use only some of the measured values to determine an inferred relationship. For example, in an embodiment, analysis module 202 may receive a value of a measured fluid quantity in the liquid state in order to determine a term that corresponds to the measured quantity (eg, A, B, C, and/or D). The analysis module 202 may include measurements of the temperature of the fluid in the liquid state in order to determine the temperature dependence of the coefficients and possibly the shift term (A). In this embodiment, the analysis module 202 may determine the corresponding coefficient that corresponds to the measured value, and multiply the measured value by the corresponding coefficient that corresponds to the measured value to form a term that corresponds to the measured value. The analysis module 202 may conduct an evaluation procedure to determine a coefficient that corresponds to the measured value using the value of the measured liquid state value and the measured energy content of the gaseous fluid. In an embodiment, the corresponding shift factor and/or term is temperature dependent such that the corresponding shift factor and/or term is not a constant. In this embodiment, the analysis module 202 may determine, through an estimation procedure, the relationship between the measured fluid temperature in the liquid state and the corresponding shift factor and/or term.

Analysis module 202 may use different inferred relationships for each of the inferred energy content types, depending on which measurements and terms are appropriate for each of the inferred energy content types. For example, one or more of measured density, measured temperature, measured viscosity, measured speed of sound, higher order values, and the like. can be used in an inferential relationship. Measurement of one or more measured values used in determining elements of the inferred relationship (e.g., constants in the form of coefficients) may be performed by system 100 using computer system 200, and/or computer system 200 may receive measured data from sources that have already defined measurements and the corresponding values of the measured energy content.

The analysis module 202 may determine or receive from the user an inferential relationship with the elements, for example, the structure of the inferred relationship (for example, relationships expressed by equations (2)-(19)) and with the relationship elements (for example, with coefficients, constants in the form coefficients and temperature and/or pressure dependent relationships to determine the coefficients potentially reflected in the relationships expressed in equations (2)-(19)). The coefficients and/or constants in terms of the coefficients of the equations and/or elements used to determine the coefficients may be determined by the analysis module 202, such as using regression or other statistical or probabilistic technology. The structure of the inferred relationship may be determined by analysis module 202 (eg, may determine the best relationship for each energy content), or may be provided by the user or meter electronics 110. The resulting elements of the inferred relationship can be associated by the analysis module 202 with one or more of the energy indicator being determined, the flow fluid, and the flow fluid class whose element is the flow fluid. Data regarding one or more of the energy indicator, fluid type, and fluid class may be provided by the user, or may be determined and/or identified by analysis module 202. The resulting inferential relationship, the elements of the relationship, and data associations therewith may be stored in the computer system 200 that determines the inferential relationship with the analysis module 202, or may be transmitted to another computer system 200, possibly electronic circuitry 110 of the vibration sensor 102 (or to directly connected hardware).

The inference module 204 uses an inferential relationship having predefined elements (eg, predefined relationships between terms and/or predefined coefficient constants) to infer inferred energy content values. The stored inferential relationship may have predefined and/or prestored elements, such as the structure of the inferred relationship (e.g., relationships expressed by equations (2)-(19)) and relationship elements (e.g., coefficients, coefficient constants, and temperature and/or pressure dependent relationships to determine the coefficients potentially reflected in the relationships expressed in equations (2)-(19)). The equation coefficients and/or elements used to determine the coefficients may be pre-determined and pre-stored in the computer system 200 (or directly connected hardware). Elements of an inferred relationship may be data-associated with one or more of the energy indicator being determined, the streaming fluid, and the streaming fluid class whose element is the streaming fluid. Data regarding one or more of the energy indicator, fluid type, and fluid class may be provided by the user or may be determined and/or identified by the inference module 204 . The data associations can ensure that the inference module 204 uses the best inference elements and power content for a particular application. The inference module 204 can retrieve, from the memory 220, the appropriate relationship elements for a particular streaming fluid and application. From this, the inference module 204 may evaluate the inferred relationship to determine the energy content of the fluid in the gaseous state from the measurements taken on the fluid in the liquid state.

In an embodiment, it should be appreciated that the determination of the elements of an inferential relationship (e.g., predefined relationships between terms and/or predefined constants in the form of coefficients) may be carried out by a first system, and the predefined elements defined in this first system, can be used in live power content inferences in the second system. In this embodiment, the computer system 200 for the first system may have one or more of an analysis module 202 and a measurement module 206, but no inference module 204. In this embodiment, the computer system 200 for the second system may have one or more of an inference module 204 and a measurement module 206, but not an analysis module 202.

In another embodiment, computer system 200 can be used to both determine elements of an inferential relationship (eg, predefined relationships between terms and predefined coefficient constants) and expand the inferred relationship to infer values energy content from live measurements under linear conditions. In this embodiment, computer system 200 may have one or more of an analysis module 202, an inference module 204, and a measurement module 206.

Measurement module 206 is a software module that receives raw data from sensors and processes the raw data to determine measurements of fluid flow and/or fluid flow. The flow fluid and/or flow fluid measurements may include one or more of measured density, pressure, viscosity, sound velocity, temperature, mass flow, and the like. In various embodiments, various hardware elements may be included in the system. Each of the various hardware elements in system 100 may have different embodiments of measurement module 206 . For example, vibration sensor 102 may measure one or more of density and viscosity using an embodiment of measurement module 206. The optional sound velocity sensor 106 may measure the sound velocity of the flowing fluid using a native embodiment of the measurement module 206 . An optional additional vibration sensor 199 may determine the mass and/or volumetric flow rate of the flowing fluid using a native embodiment of the measurement module 206 .

The characteristics of the analysis module 202, the inference module 204, and/or the measurement module 206 are assumed and reflect the methods that are performed in the presented flowcharts. All methods in this detailed description are assumed with respect to each flowchart of the method flow and the indicated orders, or when it is indicated that the order does not matter, the flowcharts of the method are reported, but all methods and characteristics of the analysis module 202, module 204 inference and measurement module 206 are intended for the purposes of all method claims that are set forth in this specification.

In addition, in embodiments where computer system 200 is meter electronics 110, meter electronics 110 may comprise a number of operably connected elements. The hardware that cooperates to form the cohesive computer system 200 that is the meter electronics 110 may have various components, such as a conventional meter electronics array operatively coupled to a suitable and/or compatible transmitter. In an embodiment, the meter electronics 110 may have at least some of its processor 210 in integral vibration sensor 102 meter electronics and at least some of the memory 220 in a transmitter coupled to the sensor. 102 vibrations.

Interface 230 is an input/output device used to operably connect computer data system 200 to external computing elements. The interface 230 is capable of connecting the computer system 200 to external elements using known technologies, the external elements including, for example, Universal Serial Bus, Prolink technology, serial communications, ATA serial interfaces, HPC connections, Gigabit Ethernet technology, Infiniband technology, and etc. Interface 230 may have a functional link module 240 . Functional link module 240 is used to connect computer system 200 to components external to computer system 200, such as external computing devices, or to facilitate communication between one or more vibration sensors 102, an optional sound velocity sensor 106, and an optional additional sensor 199 vibrations. In an embodiment where computer system 200 is meter electronics 110 composed of multiple compatible and potentially separably connectable elements (e.g., conventional vibration sensor 102 and transmitter meter electronics), functional link module 240 may operably connect the elements. . In an embodiment, functional communication module 240 may be an embodiment of communication link 26 .

Flowcharts

Fig. 3-5 show flowcharts of method embodiments for inferring and using energy content. The methods disclosed in the flowcharts are non-exhaustive and simply demonstrate potential embodiments of the steps and orders. The methods are to be construed in the context of the entire description of the invention, including in the context of the elements disclosed in the descriptions of FIG. 1 and 2, system 100 and computer system 200 disclosed in FIG. 1 and 2, analysis module 202, inference module 204, and/or measurement module 206.

Fig. 3 shows a flowchart of an embodiment of a method 300 for energy content inference. System 100, vibration sensor 102, optional sound velocity sensor 106, optional additional vibration sensor 199, computer system 200, analysis module 202, inference module 204, and measurement module 206, referred to or implicitly used in method 300, may be a system 100, vibration sensor 102, optional sound velocity sensor 106, optional additional vibration sensor 199, computer system 200, analysis module 202, inference module 204, and measurement module 206 referred to in method 300 as disclosed in FIG. 1 and 2, although any suitable system 100, vibration sensor 102, optional sound velocity sensor 106, optional additional vibration sensor 199, computer system 200, analysis module 202, inference module 204, and measurement module 206 referred to or implicitly used in method 300 , may be used in alternative embodiments. All methods for performing these steps disclosed in this detailed description are contemplated, including all features of system 100.

Step 302 infers, via inference module 204, the inferred fluid energy content in the gaseous state from the inferred relationship between the inferred energy content of the fluid in the gaseous state with at least one measurement taken of the fluid in the liquid state. Step 302 may be conducted by inference module 204 of vibration sensor 102 and/or an optional additional vibration sensor 199. The inference may be based on relationships expressed in one or more equations (2)-(19). The input parameter values for the inference may be provided by one or more of the vibration sensor 102, the optional sound velocity sensor 106, and the optional additional vibration sensor 199.

In other embodiments, the method shown in FIG. 3 may have other steps besides or instead of the step mentioned above. Subsets of the step mentioned above as part of the method shown in FIG. 3 can be used to form your own method. Method step 300 may be repeated in any combination and order any number of times, such as continuously cycling to provide live or continuous inferred energy content values under linear conditions.

Fig. 4 shows a flowchart of an embodiment of a method 400 for energy content inference. System 100, vibration sensor 102, optional sound velocity sensor 106, optional additional vibration sensor 199, computer system 200, analysis module 202, inference module 204, and measurement module 206, referred to or implicitly used in method 400, may be system 100, vibration sensor 102, optional sound velocity sensor 106, optional additional vibration sensor 199, computer system 200, analysis module 202, inference module 204, and measurement module 206 referred to in method 400 as disclosed in FIG. 1 and 2, although any suitable system 100, vibration sensor 102, optional sound velocity sensor 106, optional additional vibration sensor 199, computer system 200, analysis module 202, inference module 204, and measurement module 206 referred to or implicitly used in method 400 , may be used in alternative embodiments. All methods for performing these steps disclosed in this detailed description are contemplated, including all characteristics of system 100. Method 400 may be an embodiment of step 302, and step 302 may be an embodiment of method 400.

Step 402 receives, via inference module 204, the measured values of the relevant fluid state input parameters in the liquid state. In an embodiment, the relevant input parameters may be one or more of density, viscosity, temperature, pressure, and sound velocity. In an embodiment, the inference module 204 may be stored in the vibration sensor 102. The vibration sensor 102 may use its own measurement module 206 to measure quantities such as one or more of the density, viscosity, and temperature of the flowing fluid. The vibration sensor 102 may receive the measured sound speed from the optional sound speed sensor 106 if the inferential relationship embodiment dictates the use of a sound speed value. In an embodiment, vibration sensor 102 may optionally receive mass flow from optional additional vibration sensor 199.

Step 404 loads, via the inference module 204, the inferred relationship between the measurements taken on the flowing fluid in the liquid state and the inferred energy content of the flowing fluid in the gaseous state. The inferential relationship stored in the meter electronics 110 may have predefined and/or prestored elements, such as the structure of the inferred relationship (e.g., relationships expressed by equations (2)-(19)) and relationship elements (e.g., coefficients, coefficient constants, and temperature and/or pressure dependent relationships to determine the coefficients potentially reflected in the relationships expressed in equations (2)-(19)). The equation coefficients and/or elements used to determine the coefficients may be pre-determined and pre-stored in the meter electronics 110 of the vibration sensor 102 (or directly connected hardware). One or more of these inferential elements may be defined in a previously implemented method, such as in an embodiment of method 500 as shown in FIG. 5. These elements can be installed in another computer system with the analysis module 202. These coefficients, structures, and/or elements may be specific to one or more of the flow fluid or class of fluids whose element is a flow fluid, for example, by computer system 200 having stored data that represents an association of at least , between one of the coefficients, constants in the form of coefficients, structures and/or elements, and one or more of the streaming fluid and the class whose element is the streaming fluid. Loading may entail specifying, by the user, a fluid or class of fluids whose element is a streaming fluid and loading associated data representing the inferred relationship. For example, an inferential relationship may be associated with natural gas mixtures to be used in natural gas mixture energy content inference. In an alternative embodiment, the vibration sensor 102 may be a specific purpose meter for a particular fluid or class of fluids with an inferred relationship loaded for a particular fluid. In yet another embodiment, the meter electronics 110 may dynamically identify the flow fluid and apply the appropriate inferential relationship associated with one or more of the identified flow fluid, the class of fluids of which the flow fluid is an element, and the energy content. , which should be used for a particular application.

Step 406 infers, via inference module 204, the inferred energy content of the gaseous streaming fluid based on measurements of the liquid streaming fluid. The inference may use an inferred relationship, such as a pre-stored and/or predefined relationship. The inferred relationship may be based on one or more of the relationships shown in equations (2)-(19). The inference module 204 may use any of the characteristics of the inference module 204 discussed in this detailed description in order to infer step 406. Step 406 may be an embodiment of step 302 and/or method 300.

Step 408 optionally infers, via inference module 204, the inferred energy content flow rate. Similar to mass or volume flow, the energy content flow can be determined by determining the energy content with the base (where the base is typically one or more of mass or volume) and applying it to the flow in the base. For example, if the base is a mass, an energy content that is based on a unit mass can be inferred, and this inferred energy content per unit mass can be applied to the measured mass flow rate to result in an inferred energy content flow rate.

In an embodiment, each of the method steps shown in FIG. 4 represents a different step. In another embodiment, although illustrated as different steps in FIG. 4, steps 402-408 may not be different steps. In other embodiments, the method shown in FIG. 4 may not have all of the above steps and/or may have other steps in addition to or instead of the steps mentioned above. The steps of the method shown in FIG. 4 may be performed in a different order. Subsets of the steps mentioned above as part of the method shown in FIG. 4 can be used to form your own method. The steps of method 400 may be repeated in any combination and order any number of times, such as continuously cycling to provide live or continuous inferred energy content values under linear conditions.

Fig. 5 shows a flowchart of an embodiment of a method 500 for energy content inference. System 100, vibration sensor 102, optional sound velocity sensor 106, optional additional vibration sensor 199, computer system 200, analysis module 202, inference module 204, and measurement module 206, referred to or implicitly used in method 500, may be a system 100, vibration sensor 102, optional sound velocity sensor 106, optional additional vibration sensor 199, computer system 200, analysis module 202, inference module 204, and measurement module 206 referred to in method 500 as disclosed in FIG. 1 and 2, although any suitable system 100, vibration sensor 102, optional sound velocity sensor 106, optional additional vibration sensor 199, computer system 200, analysis module 202, inference module 204, and measurement module 206 referred to or implicitly used in method 500 , may be used in alternative embodiments. All methods for performing these steps disclosed in this detailed description are contemplated, including all features of system 100.

Step 502 receives, via the analysis module 202, the measured values of the relevant input parameters. In an embodiment, the relevant input parameters may be one or more of density, viscosity, temperature, pressure, energy content (possibly fluid in gaseous state), and sound velocity. In an embodiment, the inference module 204 may be stored in the vibration sensor 102. The vibration sensor 102 may use its own measurement module 206 to measure quantities such as one or more of the density, viscosity, and temperature of the flowing fluid. The vibration sensor 102 may receive the measured sound speed from the optional sound speed sensor 106 if the inferential relationship embodiment dictates the use of a sound speed value.

Step 504 receives or determines, via analysis module 202, the structure of the inferred relationship between the inferred energy content of the gaseous state of the flowing fluid and the received measurements of the flowing fluid in the liquid state. The parsing module 202 may store a user-supplied preferred structure for the inferential relationship, or the parsing module 202 may optimize and determine the best structure for the inferential relationship by trying many different inferential relationship structures and determining which one is the best based on a comparison of the results. (perhaps by running this method repeatedly with different streaming fluids to determine the optimal relationship pattern specific to the streaming fluid or class of fluid of which the streaming fluid is an element). Exemplary inferential relationship structures are shown in equations (2)-(19).

Step 506 determines, by analysis module 202, from the received measurements and the received or determined structure, the relationship elements. These relationship elements may include coefficients and constants in the form of relationship coefficients expressed in terms of relationships represented by one or more of equations (2)-(19). These relationship elements may be specific to a streaming fluid or to a class of fluids whose element is a streaming fluid or to which a streaming fluid is otherwise related. Block 506 may be the essence of determining an inferential relationship. A certain inferred relationship may differ by the structure and elements of the relationship, possibly for a given fluid. Step 506 may use a regression or other analysis technique performed on the structure into which the measured values are entered. The analysis can be used to determine the elements of the relationship that best enable an inferential relationship having a selected structure, using measured input values, to allow the conclusion of the inferred energy content to converge through the inferential relationship to the measured energy content of the fluid in the gaseous state, which corresponds to the actual input of the measured values. By converging the measured energy content of the fluid in the gaseous state with the inferred energy content generated by the inferred relationship based on the measurements made on the fluid in the liquid state, relationship elements can be determined that can be used in subsequent energy content inferences.

Step 508 is an optional association, by analysis module 202, of certain relationship and/or structure elements with the flowing fluid. Additional associations may be included, such as associations with relationships for the energy content used. The associations may be stored in the computer system 200 such that the relationship elements and/or structure are associated with one or more of the streaming fluid, the class of fluids whose element is the streaming fluid or to which the streaming fluid belongs, and/or a specific used indicator of energy content. Data representing one or more of the relationship and structure elements may be stored and/or associated with data identifying one or more of the flow fluid, associated fluid class, or energy indicator used. The association may be stored in memory 220.

Step 510 is an optional transfer, by parsing module 202, of data representing one or more of structures, interconnections, and associations to another computer system 200. Other computer system 200 may be a computer system that does not have parsing module 202. Another computer system 200 may be the meter electronics 110 of the vibration sensor 102. Another computer system 200 may use this data as predetermined and/or prestored data to infer the energy content of the flow fluid, perhaps even live inferences thereof.

In an embodiment, each of the method steps shown in FIG. 5 represents a different stage. In another embodiment, although illustrated as different steps in FIG. 5, steps 502-510 may not be different steps. In other embodiments, the method shown in FIG. 5 may not have all of the above steps and/or may have other steps in addition to or instead of the steps mentioned above. The steps of the method shown in FIG. 5 may be performed in a different order. Subsets of the steps mentioned above as part of the method shown in FIG. 5 can be used to form your own method. The steps of method 500 may be repeated in any combination and order any number of times, such as continuously cycling to provide live or continuous inferred energy content values under linear conditions.

Graphs

Fig. 6-10 show graphs illustrating embodiments of the inferential relationships for the energy content inferences described in the specification. These plots demonstrate the effectiveness of inferring the energy content of a gaseous flow fluid based on measurements of a liquid state flow fluid.

Natural gas mixtures typically predominantly consist of methane, with lesser relevant amounts of one or more of ethane and propane. Other petroleum substances such as higher order hydrocarbons and other substances may be present to a lesser extent. Natural gas typically consists of 80% to 99% methane, with ethane content ranges ranging from 1% to 14%. Because the compositions have these relatively consistent relationships, inferred relationships can be based on measurements that are made in the liquid phase.

Temperature and density for base alkanes in the liquid state may have a virtually linear relationship. The relationship between viscosity and temperature and basic alkanes can be more quadratic. An inferential relationship can use these correlations to infer the energy content of the flow fluid using measurements of the flow fluid that are better associated with the relative composition of the flow fluid than directly derived thermal properties of each component. For all graphs, the inferential relationship coefficients are assumed to have a second order temperature dependence as shown in equation (19). It should be appreciated that despite embodiments using a second order temperature dependence, embodiments are contemplated in which the coefficients are constant values or have temperature dependences of different orders. Embodiments are contemplated where the coefficients are also pressure dependent; however, the effects of pressure may be small due to the measurement of a fluid that is highly likely to be practically compressible.

Fig. 6 shows a plot 600 of the correspondence between measured Wobbe index values and inferred Wobbe index values inferred from an inferred relationship embodiment. Plot 600 has a plurality of data points 602 representing the relative values of the inferred and measured Wobbe index, a trend line 604, an abscissa 610 representing the measured flow fluid Wobbe index, and an ordinate 620 representing the inferred flow fluid Wobbe index inferred using inferential relationship.

An embodiment of the inferential relationship is a variation of Equation (17), with the inferential relationship being represented by Equation (24):

(24)

(24)

In equation (24), WI _Gas is the Wobbe index of the flowing fluid in gaseous state, K values (i.e. K ₁ (T), K ₂ (T), K ₅ (T), K ₄ (T), K ₆ (T)) are coefficients (temperature dependent in this embodiment), ρ _liquid is the density of the flow fluid in the liquid state, and η _liquid is the viscosity of the flow fluid in the liquid state. Here, it can be seen that the fit is excellent with a determination ratio (R ² value) of 0.996.

Fig. 7 shows a plot 700 of the correspondence between measured methane numbers and inferred methane numbers inferred from an inferred relationship embodiment. Plot 700 has a plurality of data points 702 representing the relative values of the inferred and measured methane number, a trend line 704, an abscissa 710 representing the measured flow fluid methane number, and an ordinate 720 representing the inferred flow fluid methane number inferred using inferential relationship.

An embodiment of an inferential relationship is a variation of Equation (17), with the inferential relationship being represented by Equation (25):

(25)

(25)

In equation (25), MN _Gas is the methane number of the flowing fluid in the gaseous state, the values of K (i.e. K ₁ (T), K ₂ (T), K ₅ (T), K ₄ (T), K ₆ (T)) are coefficients (temperature dependent in this embodiment), ρ _liquid is the density of the flow fluid in the liquid state, and η _liquid is the viscosity of the flow fluid in the liquid state. Here, it can be seen that the fit is excellent with a determination ratio (R ² value) of 0.994.

Fig. 8 shows a graph 800 of the correspondence between the measured lower flammable limit and the inferred lower flammable limit inferred from the inferred relationship embodiment. Plot 800 has a plurality of data points 802 representing the relative values of the inferred and measured LFL, a trend line 804, an abscissa 810 representing the measured LFL of the flow fluid, and an ordinate 820 representing the inferred LFL of the flow fluid, logically inferred using an inferred relationship.

An embodiment of an inferential relationship is a variation of Equation (17), with the inferential relationship being represented by Equation (26):

(26)

(26)

In equation (26), LFL _Gas is the lower flammability limit of the flowing fluid in the gaseous state, the values of K (i.e. K ₁ (T), K ₂ (T), K ₅ (T), K ₄ (T), K ₆ (T)) are coefficients (temperature dependent in this embodiment), ρ _liquid is the density of the flow fluid in the liquid state, and η _liquid is the viscosity of the flow fluid in the liquid state. Here, it can be seen that the fit is excellent with a determination ratio (R ² value) of 0.978.

Fig. 9 shows a plot 900 of the correspondence between the measured gross calorific value and the inferred gross calorific value inferred from the inferred relationship embodiment. Plot 900 has a plurality of data points 902 representing the relative inferred and measured gross calorific values, a trend line 904, an abscissa 910 representing the measured gross calorific value of the flow fluid, and an ordinate 920 representing the inferred gross calorific value of the flow fluid inferred using inferential relationship.

An embodiment of the inferential relationship is a variation of Equation (17), wherein the inferential relationship is represented by Equation (27):

(27)

(27)

In equation (27), GNV _Gas is the gross calorific value of the flowing fluid in gaseous state, K values (i.e. K ₁ (T), K ₂ (T), K ₅ (T), K ₄ (T), K ₆ (T)) are coefficients (temperature dependent in this embodiment), ρ _liquid is the density of the flow fluid in the liquid state, and η _liquid is the viscosity of the flow fluid in the liquid state. Here, it can be seen that the fit is excellent with a determination ratio (R ² value) of 0.997.

Fig. 10 shows a plot 1000 of the correspondence between the measured net calorific value and the inferred net calorific value inferred from the inferred relationship embodiment. Plot 1000 has a plurality of data points 1002 representing the relative inferred and measured net calorific values, a trend line 1004, an abscissa 1010 representing the measured net calorific value of the flow fluid, and an ordinate 1020 representing the inferred net calorific value of the flow fluid inferred using inferential relationship.

An embodiment of the inferential relationship is a variation of Equation (17), with the inferential relationship being represented by Equation (28):

(28)

(28)

In equation (28), NHV _Gas is the net calorific value of the flowing fluid in the gaseous state, the values of K (i.e. K ₁ (T), K ₂ (T), K ₅ (T), K ₄ (T), K ₆ (T)) are coefficients (temperature dependent in this embodiment), ρ _liquid is the density of the flow fluid in the liquid state, and η _liquid is the viscosity of the flow fluid in the liquid state. Here, it can be seen that the fit is excellent with a determination ratio (R ² value) of 0.997.

The detailed descriptions of the above embodiments do not represent complete descriptions of all embodiments intended by the inventors to be within the scope of the present description. In fact, those skilled in the art will recognize that certain elements of the above described embodiments may be combined or omitted in various ways to create additional embodiments, and such additional embodiments fall within the scope and spirit of the present disclosure. It should also be apparent to those skilled in the art that the above described embodiments may be combined in whole or in part to create additional embodiments within the scope and spirit of the present disclosure. When specific numbers representing parameter values are specified, ranges between all of these numbers, as well as ranges above and below these numbers, are assumed and disclosed.

Thus, although specific embodiments are described herein by way of illustration, various equivalent modifications are possible within the scope of the present description, as will be recognized by those skilled in the art. The ideas provided herein may apply to other methods and equipment for inferring the calorific value of a gaseous fluid from measurements made on a fluid in the liquid state, and not only to the embodiments described above and shown in the accompanying drawings. Accordingly, the scope of the embodiments described above is to be determined from the appended claims.

Claims (25)

1. A method for determining the energy content of a flowing fluid in a gaseous state by means of a computer system (200) having a processor (210) and a storage device (220), wherein the storage device (220) has an inference module (204), the method comprising the step, which infers, by means of an inference module (204), the energy content of the flowing fluid in the gaseous state from the inferred relationship between the energy content of the flowing fluid in the gaseous state with at least one measurement made for the flowing fluid in the liquid state, and at least one measurement contains the measured density and the energy content is the energy of the fluid during combustion. 2. The method of claim 1, wherein the at least one measurement further comprises one or more of the measured viscosity and the measured speed of sound. 3. The method according to paragraphs. 1, 2, in which the inferred relationship is the sum of the terms, with each term having one or more of one, at least one dimension and one higher order value of one, at least one dimension. 4. The method of claim 3, wherein each term has a coefficient that corresponds to the term. 5. The method of claim 4, wherein each coefficient is temperature dependent, wherein the temperature dependence of each coefficient has at least one member-specific constant as a coefficient. 6. The method of claim 5, wherein the relationship has at least five members, wherein the at least five members comprise: - shift term; is a measured density term having a measured density; is a higher order density term having a higher order value of the measured density; - at least one of the measured viscosity term and the speed of sound term, respectively, having one or more of the measured viscosity and the measured speed of sound; And at least one of a higher order viscosity term or a higher order speed of sound term, respectively, having one or more of the higher order value of one or more of the measured viscosity and the measured speed of sound. 7. The method according to paragraphs. 1-6, wherein the energy content is also one of methane number, lower flammability limit, Wobbe index, gross calorific value, and net calorific value, and the flowing fluid is a natural gas mixture. 8. The method according to paragraphs. 1-7, in which the computer system (200) is the electronic circuits (110) of the meter of the vibration sensor (102), containing a Coriolis flow meter, a fork density meter, a fork viscometer, and the vibration sensor (102) measures the density. 9. Equipment for determining the energy content of a flowing fluid in a gaseous state, moreover, the equipment has a computer system (200), moreover, the computer system (200) has a processor (210) and a memory device (220), moreover, the memory device (220) has a module (204 ) inference, wherein the inference module (204) is configured to infer the energy content of the flow fluid in the gaseous state from the inferred relationship between the inferred energy content of the flow fluid in the gaseous state with at least one measurement taken for the flow fluid media in a liquid state, and at least one measurement contains the measured density and the energy content is the energy of the fluid during combustion. 10. The equipment of claim 9, wherein the at least one measurement further comprises one or more of a measured viscosity and a measured speed of sound. 11. Equipment according to paragraphs. 9, 10, in which the inferred relationship is the sum of the terms, wherein each term has one or more of one, at least one dimension and one higher order value of one, at least one dimension, and each member has a coefficient that corresponds to the term. 12. The equipment of claim 11, wherein each coefficient is temperature dependent, wherein the temperature dependence of each coefficient has at least one member-specific constant as a coefficient. 13. Equipment according to claim 12, in which the relationship has at least five members, and at least five members contain: - shift term; is a measured density term having a measured density; is a higher order density term having a higher order value of the measured density; - at least one of the measured viscosity term and the speed of sound term, respectively, having one or more of the measured viscosity and the measured speed of sound; And at least one of a higher order viscosity term or a higher order speed of sound term, respectively, having one or more of the higher order value of one or more of the measured viscosity and the measured speed of sound. 14. Equipment according to paragraphs. 9-13, wherein the energy content is further also one of methane number, lower flammability limit, Wobbe index, gross calorific value, and net calorific value, and the flowing fluid is a natural gas mixture. 15. Equipment according to paragraphs. 9-14, wherein the equipment is a vibration sensor (102), a Coriolis flow meter, a fork density meter, a fork viscometer, while the computer system (200) is the electronic circuits (110) of the vibration flow sensor (102) meter, and the sensor (102 ) vibration measures density.

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