Correcting for two-phase flow in a digital flowmeter

• US 7,784,360 B2
• Filed: 07/14/2008
• Issued: 08/31/2010
• Est. Priority Date: 11/22/1999
• Status: Expired due to Term

• Current Assignee: Schneider Electric Systems USA, Inc. (Schneider Electric SE)
• Original Assignee: Invensys Systems Incorporated (Invensys PLC)
• Inventors: De La Fuente, Maria Jesus, Henry, Manus P.
• Primary Examiner(s): THOMPSON, JEWEL VERGIE
• Application Number:
  US12/172,757
• Publication Number:
  US 20090019947A1
• Time in Patent Office: 778 Days
• Field of Search: 738/613.57, 738/613.55, 738/613.56
• US Class Current: 73/861.356
• CPC Class Codes: G01F 1/74 Devices for measuring flow of a fluid or flow of a fluent solid material in suspension in another fluid
• CPC Class Codes: G01F 1/8436 signal processing
• CPC Class Codes: G01F 1/8486 with multiple measuring conduits
• CPC Class Codes: G01F 15/024 involving digital counting

Abstract

A flowmeter is disclosed. The flowmeter includes a vibratable conduit, and a driver connected to the conduit that is operable to impart motion to the conduit. A sensor is connected to the conduit and is operable to sense the motion of the conduit and generate a sensor signal. A controller is connected to receive the sensor signal. The controller is operable to detect a single-phase flow condition and process the sensor signal using a first process during the single-phase flow condition to generate a validated mass-flow measurement. The controller is also operable to detect a two-phase flow condition and process the sensor signal using a second process during the two-phase flow condition to generate the validated mass-flow measurement.

Claims

1. A system comprising:

a vibratable conduit;

a driver connected to the conduit and configured to impart motion to the conduit;

a sensor connected to the conduit and configured to sense the motion of the conduit and generate a sensor signal; and

a controller connected to receive the sensor signal, the controller being configured to;

generate a raw mass-flow measurement from the sensor signal;

generate an observed density measurement from the sensor signal;

detect a single-phase flow condition and process the raw mass-flow measurement using a first process during the single-phase flow condition to generate a first mass-flow measurement; and

detect a two-phase flow condition and using a second process during the two-phase flow condition to generate a second mass-flow measurement, wherein the second process includes (i) generating an error correction factor based, at least in part, on the raw mass-flow measurement and the observed density measurement and (ii) correcting the raw mass-flow measurement using the error correction factor.

2. The system of claim 1 wherein, to generate an error correction factor based, at least in part, on the raw mass-flow measurement and the observed density measurement, the controller is configured to determine a density drop measurement based on the observed density measurement and generate the error correction factor based on the density drop measurement in addition to the raw mass-flow measurement.

3. The system of claim 2 wherein, to generate the error correction factor based on the density drop measurement in addition to the raw mass-flow measurement, the controller is configured to input the raw mass-flow measurement and the density drop measurement into a neural network processor to generate the error correction factor used to correct the raw mass-flow measurement.

4. The system of claim 3 wherein the neural network processor is configured to receive at least one input parameter in addition to the raw mass flow rate and the density drop measurement, and apply a set of predetermined coefficients to the additional input parameter, the raw mass-flow rate, and the density drop measurement.

5. The system of claim 4 wherein the additional input parameter includes one of a temperature parameter, or a damping parameter.

6. The system of claim 4 further including a training module connected to the neural network processor to calculate an updated set of coefficients when supplied with training data.

7. The system of claim 1 wherein the second mass-flow measurement is a validated mass-flow measurement comprising a corrected mass-flow measurement generated from the raw mass-flow measurement and an uncertainty parameter calculated by the controller.

8. A flowmeter transmitter comprising:

one or more processing devices configured to;

impart motion to a vibratable conduit using a driver connected to the conduit;

receive a sensor signal from a sensor connected to the conduit and configured to sense the motion of the conduit and generate the sensor signal;

generate a raw mass-flow measurement from the sensor signal;

generate an observed density measurement from the sensor signal;

detect a single-phase flow condition and process the raw mass-flow measurement using a first process during the single-phase flow condition to generate a first mass-flow measurement; and

detect a two-phase flow condition and using a second process during the two-phase flow condition to generate a second mass-flow measurement, wherein the second process includes (i) generating an error correction factor based, at least in part, on the raw mass-flow measurement and the observed density measurement and (ii) correcting the raw mass-flow measurement using the error correction factor.

9. The flowmeter transmitter of claim 8 wherein, to generate an error correction factor based, at least in part, on the raw mass-flow measurement and the observed density measurement, the one or more processing devices are configured to determine a density drop measurement based on the observed density measurement and generate the error correction factor based on the density drop measurement in addition to the raw mass-flow measurement.

10. The flowmeter transmitter of claim 9 wherein, to generate the error correction factor based on the density drop measurement in addition to the raw mass-flow measurement, the one or more processing devices are configured to input the raw mass-flow measurement and the density drop measurement into a neural network processor to generate the error correction factor used to correct the raw mass-flow measurement.

11. The flowmeter transmitter of claim 10 wherein the neural network processor is configured to receive at least one input parameter in addition to the raw mass flow rate and the density drop measurement, and apply a set of predetermined coefficients to the additional input parameter, the raw mass-flow rate, and the density drop measurement.

12. The flowmeter transmitter of claim 11 wherein the additional input parameter includes one of a temperature parameter, or a damping parameter.

13. The flowmeter transmitter of claim 11 further including a training module connected to the neural network processor to calculate an updated set of coefficients when supplied with training data.

14. The flowmeter transmitter of claim 8 wherein the second mass-flow measurement is a validated mass-flow measurement comprising a corrected mass-flow measurement generated from the raw mass-flow measurement and an uncertainty parameter calculated by the controller.

15. A method comprising:

imparting motion to a vibratable conduit using a driver connected to the conduit;

receiving a sensor signal from a sensor connected to the conduit and configured to sense the motion of the conduit and generate the sensor signal;

generating a raw mass-flow measurement from the sensor signal;

generating an observed density measurement from the sensor signal;

detecting a single-phase flow condition and process the raw mass-flow measurement using a first process during the single-phase flow condition to generate a first mass-flow measurement; and

detecting a two-phase flow condition and using a second process during the two-phase flow condition to generate a second mass-flow measurement, wherein the second process includes (i) generating an error correction factor based, at least in part, on the raw mass-flow measurement and the observed density measurement and (ii) correcting the raw mass-flow measurement using the error correction factor.

16. The method of claim 15 wherein generating an error correction factor based, at least in part, on the raw mass-flow measurement and the observed density measurement includes determining a density drop measurement based on the observed density measurement and generating the error correction factor based on the density drop measurement in addition to the raw mass-flow measurement.

17. The method of claim 16 wherein generating the error correction factor based on the density drop measurement in addition to the raw mass-flow measurement includes inputting the raw mass-flow measurement and the density drop measurement into a neural network processor to generate the error correction factor used to correct the raw mass-flow measurement.

18. The method of claim 17 further comprising inputting into the neural network at least one input parameter in addition to the raw mass flow rate and the density drop measurement such that the neural network applies a set of predetermined coefficients to the additional input parameter, the raw mass-flow rate, and the density drop measurement.

19. The method of claim 18 wherein the additional input parameter includes one of a temperature parameter, or a damping parameter.

20. The method of claim 18 further comprising calculating an updated set of coefficients when supplied with training data.

21. The method of claim 15 wherein the second mass-flow measurement is a validated mass-flow measurement comprising a corrected mass-flow measurement generated from the raw mass-flow measurement and an uncertainty parameter calculated by the controller.