Decision support for operations and maintenance (DSOM) system

• US 20040102924A1
• Filed: 11/27/2002
• Published: 05/27/2004
• Est. Priority Date: 11/27/2002
• Status: Active Grant

• Current Assignee: Daniel R. Sisk, Darrel D. Hatley, Daryl R. Brown, Donald B. Jarrell, Elizabeth J. Eschbach, Gary R. Keibel, Jorge F. Reyes-Spindola, Kenneth R. Yates, Kevin J. Adams, Krishnan Gowri, Rex C. Stratton, Richard J. Meador
• Original Assignee: Daniel R. Sisk, Darrel D. Hatley, Daryl R. Brown, Donald B. Jarrell, Elizabeth J. Eschbach, Gary R. Keibel, Jorge F. Reyes-Spindola, Kenneth R. Yates, Kevin J. Adams, Krishnan Gowri, Rex C. Stratton, Richard J. Meador
• Inventors: Yates, Kenneth R., Stratton, Rex C., Eschbach, Elizabeth J., Hatley, Darrel D., Gowri, Krishnan, Adams, Kevin J., Reyes-Spindola, Jorge F., Meador, Richard J., Jarrell, Donald B., Sisk, Daniel R., Keibel, Gary R., Brown, Daryl R.
• Granted Patent: US 7,016,742 B2
• Time in Patent Office: Days
• US Class Current: 702/181
• CPC Class Codes: F01K 13/02 Controlling, e.g. stopping or starting
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202

Abstract

A method for minimizing the life cycle cost of processes such as heating a building. The method utilizes sensors to monitor various pieces of equipment used in the process, for example, boilers, turbines, and the like. The method then performs the steps of identifying a set optimal operating conditions for the process, identifying and measuring parameters necessary to characterize the actual operating condition of the process, validating data generated by measuring those parameters, characterizing the actual condition of the process, identifying an optimal condition corresponding to the actual condition, comparing said optimal condition with the actual condition and identifying variances between the two, and drawing from a set of pre-defined algorithms created using best engineering practices, an explanation of at least one likely source and at least one recommended remedial action for selected variances, and providing said explanation as an output to at least one user.

Claims

1. ) A method for minimizing the life cycle cost of processes comprising the following steps:

a) identifying a set optimal operating conditions for the process

b) identifying and measuring parameters necessary to characterize the actual operating condition of the process,

c) validating data generated by measuring said parameters,

d) characterizing the actual condition using said data,

e) identifying an optimal condition corresponding to the actual condition,

f) comparing said optimal condition with said actual condition and identifying variances between the two,

g) drawing from a set of pre-defined algorithms an explanation of at least one likely source and at least one recommended remedial action for selected variances, and

h) providing said explanation as an output to at least one user.

2. ) The method of claim 1) wherein said process is selected from the group consisting of nuclear processes, nuclear electrical power generating plants, nuclear waste vitrification and packaging plants, nuclear fuel manufacturing and reprocessing facilities, chemical processes, special chemical manufacturing involving carcinogens or pesticides, general chemical manufacture, fertilizer manufacture, plastics manufacture, pharmaceuticals manufacture, petroleum refining, aluminum smelting, steel production, automobile manufacturing, food preparation, heating and cooling, microchip manufacturing, machining processes, fossil fuel electrical generation, and combined heat and power installations.

3. ) The method of claim 1) wherein the set of optimal and actual operating conditions are selected from the group consisting of conditions relating to a plant, conditions relating to a building, and combinations thereof.

4. ) The method of claim 3) wherein the set of optimal and actual operating conditions relating to a plant are selected from the group consisting of conditions relating to at least one boiler, conditions relating to the balance of the plant, conditions relating to at least one turbine, and combinations thereof.

5. ) The method of claim 4) wherein a diagnostic function of the predefined algorithms for variances between conditions relating to at least one boiler are selected from the group consisting of the identification of boiler tube fouling, a boiler tube leak, a boiler water level control malfunction, a burner nozzle or firing rate control malfunction, excessive fire box heat loss, an economizer tube fouling, an economizer tube leak, an induced draft fan problem, a forced draft fan problem, stack condensation, a steam drum water level control malfunction, over firing, and combinations thereof.

6. ) The method of claim 4) wherein a diagnostic function of the predefined algorithms for variances between conditions relating to the balance of the plant is the identification of a high pressure steam leak.

7. ) The method of claim 4) wherein a diagnostic function of the predefined algorithms for variances between conditions relating to at least one turbine are selected from the group consisting of identification of a salt water cooling problem, a loss of condenser vacuum, condenser tube fouling, a turbine condensate control problem, and combinations thereof.

8. ) The method of claim 3) wherein the set of optimal operating and actual conditions relating to the building are selected from the group consisting of conditions relating to space heating, conditions relating to hot water generation, and combinations thereof.

9. ) The method of claim 8) wherein a diagnostic function of the predefined algorithms for variances between conditions relating to space heating are selected from the group consisting of identification of a space heating system fault, building heat loss, a zone valve malfunction, and combinations thereof.

10. ) The method of claim 8) wherein a diagnostic function of the predefined algorithms for variances between conditions relating to hot water generation are selected from the group consisting of identification of high domestic hot water energy use, air or water binding in domestic hot water tank coils, domestic hot water tank coil failure, and combinations thereof.

11. ) A method for minimizing the life cycle cost of the process of heating of a building comprising the following steps:

a) identifying a set optimal operating conditions for the process of heating the building,

b) identifying and measuring parameters necessary to characterize the actual operating condition of the process,

c) validating data generated by measuring said parameters,

d) characterizing the actual condition using said data,

e) identifying an optimal condition corresponding to the actual condition,

f) comparing said optimal condition with said actual condition and identifying variances between the two,

g) drawing from a set of pre-defined algorithms an explanation of at least one likely source and at least one recommended remedial action for selected variances, and

h) providing said explanation as an output to at least one user.

12. ) The method of claim 11) wherein the set of optimal and actual operating conditions are selected from the group consisting of conditions relating to a plant, conditions relating to a building, and combinations thereof.

13. ) The method of claim 12) wherein the set of optimal and actual operating conditions relating to a plant are selected from the group consisting of conditions relating to at least one boiler, conditions relating to the balance of the plant, conditions relating to at least one turbine, and combinations thereof.

14. ) The method of claim 13) wherein a diagnostic function of the predefined algorithms for variances between conditions relating to at least one boiler are selected from the group consisting of identification of boiler tube fouling, a boiler tube leak, a boiler water level control malfunction, a burner nozzle or firing rate control malfunction, excessive fire box heat loss, an economizer tube fouling, an economizer tube leak, an induced draft fan problem, a forced draft fan problem, stack condensation, a steam drum water level control malfunction, over firing, and combinations thereof.

15. ) The method of claim 13) wherein a diagnostic function of the predefined algorithms for variances between conditions relating to the balance of the plant is identification of a high pressure steam leak.

16. ) The method of claim 13) wherein a diagnostic function of the predefined algorithms for variances between conditions relating to at least one turbine are selected from the group consisting of identification of a salt water cooling problem, a loss of condenser vacuum, condenser tube fouling, a turbine condensate control problem, and combinations thereof.

17. ) The method of claim 12) wherein the set of optimal operating and actual conditions relating to the building are selected from the group consisting of conditions relating to space heating, conditions relating to hot water generation, and combinations thereof.

18. ) The method of claim 17) wherein a diagnostic function of the predefined algorithms for variances between conditions relating to space heating are selected from the group consisting of identification of a space heating system fault, building heat loss, a zone valve malfunction, and combinations thereof.

19. ) The method of claim 17) wherein a diagnostic function of the predefined algorithms for variances between conditions relating to hot water generation are selected from the group consisting of identification of high domestic hot water energy use, air or water binding in domestic hot water tank coils, domestic hot water tank coil failure, and combinations thereof.