Abnormal situation prevention in a heat exchanger

• US 8,762,106 B2
• Filed: 09/28/2007
• Issued: 06/24/2014
• Est. Priority Date: 09/28/2006
• Status: Active Grant

• Current Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Original Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Inventors: Miller, John Philip
• Primary Examiner(s): Kundu, Sujoy
• Assistant Examiner(s): BAILEY, COREY M
• Application Number:
  US11/864,727
• Publication Number:
  US 20080082304A1
• Time in Patent Office: 2,461 Days
• Field of Search: 702/33, 702/34, 702/35, 702/45, 702/184, 702/185, 700/52, 703/2
• US Class Current: 702/185
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G05B 23/021 adopting a different treatment of each operating region or a different mode of the monitored system, e.g. transient modes; different operating configurations of monitored system
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks

Abstract

A system and method to facilitate the monitoring and diagnosis of a process control system and any elements thereof is disclosed with a specific premise of abnormal situation prevention in a heat exchanger. Monitoring and diagnosis of faults in a heat exchanger includes statistical analysis techniques, such as regression and load following. In particular, on-line process data is collected from an operating heat exchanger. A statistical analysis is used to develop a regression model of the process. The output may use a variety of parameters from the model and may include normalized process variables based on the training data, and process variable limits or model components. Each of the outputs may be used to generate visualizations for process monitoring and diagnostics and perform alarm diagnostics to detect abnormal situations in the heat exchanger.

Claims

1. A non-transitory computer-readable storage medium comprising computer-executable instructions for detecting an abnormal situation during operation of a heat exchanger within a process in a process plant, the instructions comprising:

collecting a plurality of first data sets for the heat exchanger while the process is producing a product by processing physical materials using one or more field devices communicatively coupled to one or more process controllers and while the heat exchanger within the process is in a first normal operating region during a first period of heat exchanger operation, the plurality of first data sets generated from a fluid flow rate variable indicating a flow rate of the fluid and a single process value, the single process value including either;

a) a thermal resistance variable or b) a differential pressure variable;

generating a first regression model of the heat exchanger in the first normal operating region from the plurality of first data sets, wherein the first regression model is a function of the flow rate;

determining a first range for the flow rate in which the first regression model is valid;

generating a model of heat exchanger operation, the model of heat exchanger operation including the first regression model, wherein the model of heat exchanger operation is a function of the flow rate;

collecting a plurality of second data sets for the heat exchanger while the heat exchanger is in a second normal operating region, the plurality of second data sets generated from the fluid flow rate variable and the single process value while the heat exchanger operates in the second normal operating region;

generating a second regression model of the heat exchanger in the second normal operating region using the plurality of second data sets, wherein the second regression model is a function of the flow rate;

determining a second range for the flow rate in which the second regression model is valid;

updating the model of heat exchanger operation to include simultaneously the first regression model for the first range of the flow rate and the second regression model for the second range of the flow rate; and

detecting an abnormal situation using the updated model of heat exchanger operation;

wherein the abnormal situation is indicated based on one or more difference values and each difference value is the difference between a monitored value of the single process value at a corresponding value of the flow rate and a modeled value of the single process value at the corresponding value of the flow rate.

2. The non-transitory computer-readable storage medium of claim 1, wherein the plurality of first data sets and the plurality of second data sets are generated from one or more of a cold fluid flow rate, a cold fluid inlet temperature, a cold fluid outlet temperature, a cold fluid inlet pressure, a cold fluid outlet pressure, the hot fluid flow rate, a hot fluid inlet temperature, a hot fluid outlet temperature, a hot fluid inlet pressure, and a hot fluid outlet pressure.

3. The non-transitory computer-readable storage medium of claim 2, wherein, if the single process value includes the differential pressure variable, the differential pressure variable comprises either a difference between the cold fluid inlet pressure and the cold fluid outlet pressure or a difference between the hot fluid inlet pressure and the hot fluid outlet pressure.

4. The non-transitory computer-readable storage medium of claim 1, wherein the plurality of first data sets comprises one or more of raw process variable data and a statistical variation of the raw process variable data.

5. The non-transitory computer-readable storage medium of claim 4, wherein, if the plurality of first and second data sets comprises, at least in part, collecting the statistical variation of the raw process variable data, the statistical variation of the raw process variable data comprises one or more of a mean, a median, or a standard deviation.

6. The non-transitory computer-readable storage medium of claim 5, further comprising computer executable instructions for modeling the standard deviation of the statistical variation of the raw process variable data as a function of a load variable, if the statistical variation of the raw process variable data comprises, at least in part, the standard deviation.

7. The non-transitory computer-readable storage medium of claim 1, further comprising computer executable instructions for modifying one or more of a cold fluid flow rate, a cold fluid inlet temperature, a cold fluid outlet temperature, a cold fluid inlet pressure, a cold fluid outlet pressure, a hot fluid flow rate, a hot fluid inlet temperature, a hot fluid outlet temperature, a hot fluid inlet pressure, and a hot fluid outlet pressure upon detecting an abnormal situation in the heat exchanger.

8. The non-transitory computer-readable storage medium of claim 1, wherein if the single process value includes the thermal resistance variable, the thermal resistance variable is a function of one or more of the group consisting of a cold fluid flow rate and a hot fluid flow rate, a cold fluid inlet temperature, a cold fluid outlet temperature, a hot fluid inlet temperature, and a hot fluid outlet temperature.

9. A system for detecting an abnormal situation during operation of a heat exchanger within a process in a process plant, comprising:

a configurable process model of the process in the process plant, the configurable process model a function of an independent process variable of the heat exchanger during operation within the process and including a first regression model in a first range of the independent variable corresponding to a first normal operating region of the process, the first regression model a function of the independent process variable, the configurable process model capable of being subsequently configured to include simultaneously with the first regression model a second regression model in a second range of the independent variable corresponding to a second normal operating region different than the first normal operating region, the second regression model a function of the independent process variable;

wherein, after the configurable process model is configured to include the second regression model simultaneously with the first regression model, the configurable process model is capable of modeling the process in the first normal operating region using the first regression model, and modeling the process in the second normal operating region using the second regression model; and

the system further comprising a deviation detector coupled to the configurable process model, the deviation detector configured to determine if the process significantly deviates from an output of the process model by comparing one or more measured values of a dependent process variable of the heat exchanger during operation within the process at a corresponding value of the independent process variable to one or more modeled values of the dependent process variable at the corresponding value of the independent process variable;

wherein the independent process variable includes either a thermal resistance variable or a differential pressure variable of the heat exchanger and the dependent process variable includes a flow rate of the heat exchanger.

10. A system according to claim 9, wherein the configurable process model is capable of being configured, after being configured to include the second regression model simultaneously with the first regression model, to include simultaneously with the first and second regression models at least a third regression model in at least a third range of the independent process variable corresponding to at least a third normal operating region different than the first normal operating region and the second normal operating region;

wherein, after the configurable process model is configured to include the third regression model simultaneously with the first and second regression models, the configurable process model is capable of modeling operation of the heat exchanger within the process in the first normal operating region using the first regression model, modeling operation of the heat exchanger within the process in the second normal operating region using the second regression model, and modeling operation of the heat exchanger within the process in the third normal operating region using the third regression model.

11. A system according to claim 9, wherein the configurable process model is capable of generating an interpolated model for a third range of the independent process variable between the first range of the independent process variable and the second range of the independent process variable.

12. A system according to claim 11, wherein the configurable process model is capable of generating a prediction of a Y value as a function of an X value, wherein the Y value is generated from a dependent process variable and the X value is generated from an independent process variable;

wherein, after the configurable process model is configured to include the second regression model simultaneously with the first regression model and after generating the interpolated model, the configurable process model is capable of;

generating the prediction of the Y value using the first regression model if the X value is in the first range of the independent process variable,

generating the prediction of the Y value using the second regression model if the X value is in the second range of the independent process variable,

generating the prediction of the Y value using the interpolated model if the X value is in the third range of the independent process variable between the first range of the independent process variable and the second range of the independent process variable.

13. A system according to claim 12, wherein the deviation detector is configured to determine if the Y value significantly differs from the predicted Y value.

14. A system according to claim 13, wherein the deviation detector is configured to determine if the Y value significantly differs from the predicted Y value by comparing a difference between the Y value and the predicted Y value to a threshold.

15. A system according to claim 14, wherein the threshold varies as a function of the X value.

16. A system according to claim 13, further comprising:

a first statistical generator coupled to the configurable process model, the first statistical generator to generate the X values as a mean of the independent process variable; and

a second statistical generator coupled to the configurable process model, the second statistical generator to generate the Y values as a mean of the dependent process variable.

17. A system according to claim 16, wherein the second statistical generator additionally generates a standard deviation values of the dependent process variable;

the system further comprising an additional model coupled to the first statistical generator, the second statistical generator, and the deviation detector, the additional model to generate a prediction of the standard deviation of the dependent process variable as a function of the mean of the independent process variable;

wherein the deviation detector is configured to determine if the Y value significantly differs from the predicted Y value by comparing a difference between the Y value and the predicted Y value to a threshold, wherein the threshold is a function of the prediction of the standard deviation.