ABNORMAL SITUATION PREVENTION IN A HEAT EXCHANGER

• US 20080082304A1
• Filed: 09/28/2007
• Published: 04/03/2008
• 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 P.
• Granted Patent: US 8,762,106 B2
• Time in Patent Office: Days
• US Class Current: 703/9
• 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 method for detecting an abnormal situation during operation of a heat exchanger, the method comprising:

collecting a plurality of first data points for the heat exchanger while the heat exchanger is in a first operating region during a first period of heat exchanger operation,

the first data points generated from one or more of a cold fluid flow rate variable or a hot fluid flow rate variable and generated from one or more of a differential pressure variable or a thermal resistance variable;

generating a regression model of the heat exchanger in the first operating region from the first data points;

inputting a plurality of second data points into the regression model, the plurality of second data points generated from the one or more of the cold fluid flow rate variable or the hot fluid flow rate variable and generated from one or more of the differential pressure variable or the thermal resistance variable during a second period of heat exchanger operation while the heat exchanger is in the first operating region;

outputting, from the regression model, a predicted value generated from one or more of the differential pressure variable or the thermal resistance variable as a function of a value generated from one or more of the cold fluid flow rate variable or the hot fluid flow rate variable during the second period of heat exchanger operation;

comparing the predicted value generated from the one or more of the differential pressure variable or the thermal resistance variable during the second period of heat exchanger operation to a respective value generated from the differential pressure variable or the thermal resistance variable during the second time period of heat exchanger operation; and

detecting an abnormal situation if the value generated from one or more of the differential pressure variable or the thermal resistance variable during the second period of heat exchanger operation significantly deviates from the respective predicted value generated from one or more of the differential pressure variable or the thermal resistance variable.

2. The method of claim 1, wherein collecting the plurality of first data points and the plurality of second data points comprise first and second data points generated from one or more of the 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 method of claim 2, wherein the differential pressure 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 method of claim 1, wherein the plurality of first data points comprises collecting one or more of raw process variable data and a statistical variation of the raw process variable data.

5. The method of claim 4, wherein the statistical variation of the raw process variable data comprises one or more of a mean, a median, or a standard deviation.

6. The method of claim 5, further comprising modeling the standard deviation of the statistical variation of the process variable data as a function of a load variable.

7. The method of claim 1, further comprising generating a new regression model of the heat exchanger in a second operating region if a second data point generated from one or more of the cold fluid flow rate variable or the hot fluid flow rate variable is observed outside the first operating region during the second period of heat exchanger operation.

8. The method of claim 1, further comprising modifying one or more of the 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 upon detecting an abnormal situation in the heat exchanger.

9. The method of claim 1, further comprising adding an ordered pair to an array to model one or more of the differential pressure variable and the thermal resistance variable as a function of one or more of the cold fluid flow rate and the hot fluid flow rate, a first value of the ordered pair including one or more of the cold fluid flow rate and the hot fluid flow rate and a second value of the ordered pair including one or more of the differential pressure variable and the thermal resistance variable.

10. The method of claim 1, wherein the thermal resistance variable is a function of one or more of the group consisting of the cold fluid flow rate and the hot fluid flow rate, the cold fluid inlet temperature, the cold fluid outlet temperature, the hot fluid inlet temperature, and the hot fluid outlet temperature.

14. The method of claim 10 further comprising:

receiving a new data point including data generated from a new fluid flow rate and from one or more of a new differential pressure and a new thermal resistance;

executing a monitoring function when the data generated from the new fluid flow rate is greater than data generated from a smallest first fluid flow rate of a point in the array and smaller than a largest first fluid flow rate of a point in the array, wherein the monitoring function further comprises;

calculating a predicted value generated from one or more of the first pressure differential or the first thermal resistance from the new point based on the value of the data generated from the first fluid flow rate of the new point and the function modeling the data generated from the plurality of first load variables as a function of the data generated from the plurality of first monitored variables;

comparing the predicted value generated from the one or more of the first pressure differential or from the first thermal resistance to the respective actual value generated from the pressure differential or from the thermal resistance of the new point; and

detecting an abnormal condition if the difference between the predicted value generated from the one or more of the first pressure differential or from the first thermal resistance and the respective actual value generated from the pressure differential or from the thermal resistance of the new point exceeds a predefined threshold.

11. A method of detecting an abnormal condition in a heat exchanger, the method comprising:

calculating data generated from a plurality of first load variables from sampled values of a plurality of heat exchanger process variables collected over a plurality of sample windows, the plurality of first load variables including a first fluid flow rate;

calculating data generated from a plurality of first monitored variables from sampled values of the plurality of heat exchanger process variables collected over a plurality of corresponding sample windows, the plurality of first monitored variables including a first differential pressure and a first thermal resistance; and

generating a function modeling the data generated from the plurality of first load variables as a function of the data generated from the plurality of first monitored variables by adding points to an array, the points comprising ordered pairs of the data generated from the plurality of first load variables and the data generated from the plurality of first monitored variables calculated from corresponding sample windows;

wherein the first fluid flow rate includes one or more of a cold fluid flow rate and a hot fluid flow rate, the first differential pressure includes one or more of a cold fluid inlet pressure, a cold fluid outlet pressure, a hot fluid inlet pressure, and a hot fluid outlet pressure, and the first thermal resistance includes the flow rate and one or more of a hot fluid inlet temperature, a hot fluid outlet temperature, a cold fluid inlet temperature, and a cold fluid outlet temperature.

12. The method of claim 11 further comprising:

receiving a new point including data generated from a new load variable value and from a corresponding new monitored variable value;

and executing a learning function that includes adding the new point to the array when the data generated from the new load variable value is either less than or greater than data generated from one of the first load variable values of all other points in the array.

13. The method of claim 11 further comprising:

limiting the array to a fixed number of points, and removing a point from the array when adding the new point will exceed the fixed number of points; and

identifying a point which, if removed from the array, will result in a least amount of error between a first function defined by the array including the point and a second function defined by the array excluding the point.

15. A method for detecting an abnormal situation during operation of a heat exchanger, the heat exchanger including a shell portion having a cold fluid inlet and a cold fluid outlet, and a tube portion having a hot fluid inlet and a hot fluid outlet, the shell portion and the tube portion including one or more flow controllers in communication with one or more flow control valves, wherein each flow controller is configured to modify a position of the one or more flow control valves to control one or more of a cold fluid flow rate or a hot fluid flow rate, the method comprising:

collecting, during a first period of heat exchanger operation, first data sets generated from a flow rate and one or more of a differential pressure and a thermal resistance, wherein the flow rate comprises one or more of a cold fluid flow rate and a hot fluid flow rate, wherein the differential pressure comprises one or more of a cold fluid inlet pressure, a cold fluid outlet pressure, a hot fluid inlet pressure, and a hot fluid outlet pressure, and wherein the thermal resistance comprises the flow rate and one or more of a cold fluid inlet temperature, a cold fluid outlet temperature, a hot fluid inlet temperature, and a hot fluid outlet temperature;

generating a regression model of the heat exchanger in a first operating region from the first data sets, wherein the flow rate corresponds to a load variable of the model and one or more of the differential pressure or the thermal resistance corresponds to a monitored variable; and

inputting data generated from the flow rate into the regression model to result in an output from the regression model of a predicted value generated from one or more of the differential pressure and the thermal resistance;

collecting, during a second period of heat exchanger operation, second data sets generated from the flow rate and generated from one or more of the differential pressure and the thermal resistance;

inputting into the regression model the second data sets generated from the flow rate recorded during the second period of heat exchanger operation;

outputting from the regression model a predicted value generated from one or more of the differential pressure and the thermal resistance;

one or more of;

comparing the predicted value generated from the differential pressure with the differential pressure, and

comparing the predicted value generated from the thermal resistance with the thermal resistance; and

detecting an abnormal situation if the value generated from at least one of the differential pressure during the second period of heat exchanger operation and the thermal resistance during the second period of heat exchanger operation significantly deviates from the predicted values generated from the differential pressure and the thermal resistance.

16. The method of claim 15, wherein the differential pressure is a function of one or more of a cold fluid inlet pressure, a cold fluid outlet pressure, a hot fluid inlet pressure, and a hot fluid outlet pressure.

17. The method of claim 15, further comprising modifying the position of the flow control valve if the value generated from one or more of the differential pressure or the thermal resistance during the second period of heat exchanger operation significantly deviates from the predicted values generated from the differential pressure and the thermal resistance.

18. The method of claim 15, further comprising generating a new regression model of the heat exchanger if the flow rate during the second period of heat exchanger operation is not within the first operating region.

19. The method of claim 15, further comprising detecting a location of the abnormal situation based on one or more of the differential pressure or the thermal resistance.

20. A system for monitoring an abnormal situation in a heat exchanger comprising:

a data collection tool adapted to collect on-line process data from the heat exchanger during operation of the heat exchanger, wherein the collected on-line process data is generated from a plurality of heat exchanger process variables;

an analysis tool comprising one or more of;

a regression analysis engine adapted to model the operation of the heat exchanger based on a set of data generated from the collected on-line process data comprising a measure of the operation of the heat exchanger when the heat exchanger is on-line, wherein the model of the operation of the heat exchanger is adapted to be executed to generate a predicted value generated from a first one of the heat exchanger process variables as a function of data generated from a second one of the heat exchanger process variables, and wherein the analysis tool is adapted to store the model of the operation of the heat exchanger and the set of data generated from the collected on-line process data, or

a load following analysis engine adapted to generate a function modeling the data generated from the first one of the heat exchanger process variables as a function of the data generated from the second one of the heat exchanger process variables by adding points to an array, the points comprising ordered pairs of the data generated from the first one of the heat exchanger process variables and the data generated from the second one of the heat exchanger process variables calculated from corresponding sample windows; and

a monitoring tool adapted to generate;

the set of data generated from the collected on-line process data,

the predicted value data generated from the first one of the heat exchanger process variables using the analysis tool, and

a heat exchanger status including a parameter of the model of the operation of the heat exchanger, wherein the parameter comprises the at least one process variable of the set of data generated from the collected on-line process data.

21. The system of claim 20, wherein the plurality of heat exchanger process variables comprises one or more of a flow rate including one or more of a cold fluid flow rate and a hot fluid flow rate, a differential pressure including one or more of a cold fluid inlet pressure, a cold fluid outlet pressure, a hot fluid inlet pressure, and a hot fluid outlet pressure, and a thermal resistance including the flow rate and one or more of a cold fluid inlet temperature, a cold fluid outlet temperature, a hot fluid inlet temperature, and a hot fluid outlet temperature.

22. A system for detecting an abnormal situation in a heat exchanger comprising:

a data collection tool adapted to collect on-line process data from the heat exchanger during operation of the heat exchanger, wherein the collected on-line process data is generated from a plurality of heat exchanger process variables;

an analysis tool comprising a regression analysis engine adapted to model the operation of the heat exchanger based on a set of data generated from the collected on-line process data comprising a measure of the operation of the heat exchanger when the heat exchanger is on-line, wherein the model of the operation of the heat exchanger is adapted to be executed to generate a predictive value generated from a first one of the plurality of heat exchanger process variables as a function of data generated from a second one of the plurality of heat exchanger process variables, and wherein the analysis tool is adapted to store the model of the operation of the heat exchanger and the set of data generated from the collected on-line process data;

a monitoring tool adapted to generate;

the set of data generated from the collected on-line process data,

the predicted value generated from the at least one of the heat exchanger process variables using the analysis tool, and

a heat exchanger status indicating a parameter of the model of the operation of the heat exchanger, wherein the parameter of the model of the operation of the heat exchanger comprises the at least one process variable of the set of data generated from the collected on-line process data;

an operator display including a representation of the heat exchanger having a plurality of operation areas;

a selectable user interface structure associated with each of the plurality of operation areas, each user interface structure adapted to display information about the associated operation area; and

an abnormal situation indicator including a graphical display associated with one or more of the plurality of operation areas, the graphical display adapted to indicate an abnormal situation of the heat exchanger during operation of the heat exchanger.

23. The system of claim 22, wherein the selectable user interface structure is adapted to enable a user to control a configurable parameter of the heat exchanger, the configurable parameter including at least one of a learning mode time period, a statistical calculation period, a regression order, a load following array size, and a threshold limit.