REGISTER  |  SIGN IN
United States Patent6795798
Eryurek , ; et al.September 21, 2004
Title
Remote analysis of process control plant data
Abstract
A system and method for analyzing a process collects process data within a process control plant and transmits the collected process data to a remote data processing facility. The remote data processing facility analyzes the process data using a process analysis tool such as a process monitoring tool, an equipment monitoring tool, a device monitoring tool, an index generation tool, a work order generation tool and/or an accounting tool to generate analysis data. The analysis data is then transmitted to the process control plant via a communication link such as the Internet.
Inventors:Eryurek; Evren (Minneapolis, MN), Smith; Ross Stephen  (Nunthorpe, GB), Dewar; Ian Bryce  (Low Worsall, GB), Raynor; Stuart Brian  (Guisborough, GB), Minto; Jeffrey Alan  (Chester-le-Street, GB)
Assignee:Fisher-Rosemount Systems, Inc. (Austin, TX)
Appl. No.:852945
Filed:May 10, 2001
Current U.S. Class:702/188 700/29 
Current International Class:G05B 23/02 (20060101)
Field of Search:702/113-115,122,182-185,187-188 376/215-216 700/28-31,95-96,108-109,117,266,275,286-291 703/7,12,18
U.S. Patent Documents
4527271July 1985Hallee et al.
4607325August 1986Horn
4630189December 1986Ohmori et al.
4657179April 1987Aggers et al.
4734873March 1988Malloy et al.
4763243August 1988Barlow et al.
4764862August 1988Barlow et al.
4885694December 1989Pray et al.
4907167March 1990Skeirik
4910691March 1990Skeirik
4944035July 1990Aagardl et al.
4956793September 1990Bonne et al.
4965742October 1990Skeirik
5006992April 1991Skeirik
5008810April 1991Kessel et al.
5015934May 1991Holley et al.
5018215May 1991Nasr et al.
5043863August 1991Bristol et al.
5050095September 1991Samad
5070458December 1991Gilmore et al.
5121467June 1992Skeirik
5134574July 1992Beaverstock et al.
5140530August 1992Guha et al.
5142612August 1992Skeirik
5161013November 1992Rylander et al.
5167009November 1992Skeirik
5173899December 1992Ballance
5187674February 1993Bonne
5193143March 1993Kaemmerer et al.
5197114March 1993Skeirik
5212765May 1993Skeirik
5224203June 1993Skeirik
5282261January 1994Skeirik
5291190March 1994Scarola et al.
5301101April 1994MacArthur et al.
5301122April 1994Halpern
5311447May 1994Bonne
5329443July 1994Bonaquist et al.
5333298July 1994Bland et al.
5351184September 1994Lu et al.
5353207October 1994Keeler et al.
5369599November 1994Sadjadi et al.
5373452December 1994Guha
5384698January 1995Jelinek
5390326February 1995Shah
5396415March 1995Konar et al.
5398303March 1995Tanaka
5408406April 1995Mathur et al.
5442544August 1995Jelinek
5486920January 1996Killpatrick et al.
5486996January 1996Samad et al.
5488697January 1996Kaemmerer et al.
5537310July 1996Tanake et al.
5541833July 1996Bristol et al.
5546301August 1996Agrawal et al.
5559690September 1996Keeler et al.
5561599October 1996Lu
5566065October 1996Hansen et al.
5570282October 1996Hansen et al.
5572420November 1996Lu
5574638November 1996Lu
5596704January 1997Geddes et al.
5640491June 1997Bhat et al.
5640493June 1997Skeirik
5666297September 1997Britt et al.
5680409October 1997Qin et al.
5687090November 1997Chen et al.
5692158November 1997Degeneff et al.
5704011December 1997Hansen et al.
5715158February 1998Chen
5729661March 1998Keeler et al.
5740324April 1998Mathur et al.
5742513April 1998Bouhenguel et al.
5761518June 1998Boehling et al.
5764891June 1998Warrior
5768119June 1998Havekost et al.
5777872July 1998He
5781432July 1998Keeler et al.
5790898August 1998Kishima et al.
5796606August 1998Spring
5796609August 1998Tao et al.
5798939August 1998Ochoa et al.
5805442September 1998Crater et al.
5809490September 1998Guiver et al.
5819050October 1998Boehling et al.
5819232October 1998Shipman
5825645October 1998Konar et al.
5826249October 1998Skeirik
5828567October 1998Eryurek et al.
5842189November 1998Keeler et al.
5847952December 1998Samad
5859773January 1999Keeler et al.
5859964January 1999Wang et al.
5877954March 1999Klimasauskas et al.
5892679April 1999He
5892939April 1999Call et al.
5898869April 1999Anderson
5901058May 1999Steinman et al.
5905989May 1999Biggs
5907701May 1999Hanson
5909370June 1999Lynch
5909541June 1999Sampson et al.
5909586June 1999Anderson
5917840June 1999Cheney et al.
5918233June 1999La Chance et al.
5924086July 1999Mathur et al.
5940290August 1999Dixon
5948101September 1999David et al.
5949417September 1999Calder
5960214September 1999Sharpe, Jr. et al.
5960441September 1999Bland et al.
5975737November 1999Crater et al.
5984502November 1999Calder
5988847November 1999McLaughlin et al.
6008985December 1999Lake et al.
6017143January 2000Eryurek et al.
6033257March 2000Lake et al.
6041263March 2000Boston et al.
6047221April 2000Piche et al.
6055483April 2000Lu
6061603May 2000Papadopoulos et al.
6067505May 2000Bonoyer et al.
6076124June 2000Korowitz et al.
6078843June 2000Shavit
6093211July 2000Hamielec et al.
6106785August 2000Havlena et al.
6108616August 2000Borchers et al.
6110214August 2000Klimasauskas
6122555September 2000Lu
6128279October 2000O'Neil et al.
6144952November 2000Keeler et al.
6157916December 2000Hoffman
6169980January 2001Keeler et al.
6188990February 2001Brook et al.
6198479March 2001Humpleman et al.
6298377October 2001Hartikainen et al.
6298454October 2001Schleiss
6317701November 2001Pyotsia et al.
6332110December 2001Wolfe
6385558May 2002Schlemm
6385562May 2002Roth et al.
6397114May 2002Eryurek et al.
6421571July 2002Spriggs et al.
6442515August 2002Varma et al.
6445963September 2002Blevins et al.
6496751December 2002Salvo et al.
6529780March 2003Soergel et al.
6556956April 2003Hunt
Foreign Patent Documents
0 822 473Feb., 1998EP
0 825 506Feb., 1998EP
0 895 197Jul., 1997EP
0 964 325Dec., 1999EP
0 965 897Dec., 1999EP
WO 00/04427Jan., 2000WO
WO 00/49471Aug., 2000WO
WO 00/70417Nov., 2000WO
WO 0062256Oct., 2000WO
WO 99/13388Mar., 1999WO
WO 99/63409Dec., 1999WO
WO200062256Oct., 2000FI
WO200165875Mar., 2001RO
Other References
Partial International Search report issued in PCT/US02/0563, Oct. 23, 2002. .
International Search report issued in PCT/US02/05635, Mar. 13, 2003. .
Patent application No. 09/499,445, dated Feb. 7, 2000, "Diagnostics Expert in a Process Control System." .
Patent application No. 09/257,896, dated Feb. 25, 1999. .
Patent application No. 09/707,580, dated Nov. 17, 2000, "Integrated Alarm Display in a Process Control Network." .
Patent application No. 09/593,327, dated Jun. 14, 2000, "Integrated Optimal Model Predictive Control in a Process Control System." .
"GE Predictor.TM. Services: GE Predictor Services Overview," GE Industrial Systems, available at http://www.geindustrial.com/cwc/services?id=71 on Jul. 18, 2002. .
"Electrical Equipment Coverage," GE Industrial Systems, available at http://www.geindustrial.com/cwc/services?id=72 on Jul. 18, 2002. .
"Customer Impact," GE Industrial Systems, available at http://www.geindustrial.com/cwc/services?id=73 on Jul. 18, 2002. .
"Components of GE Predictor.TM. Services," GE Industrial Systems, available at http://www.geindustrial.com/cwc/services?id=74 on Jul. 18, 2002. .
"Customer Benefits," GE Industrial Systems, available at http://www.geindustrial.com/cwc/services?id=75 on Jul. 18, 2002. .
Partial International Search report issued in PCT/US05/0563, Oct. 23, 2002..~
Primary Examiner: Hoff; Marc S.
Assistant Examiner: Baran; Mary Catherine
Attorney, Agent or Firm:Marshall, Gerstein & Borun LLP
Parent Case Text


RELATED APPLICATION

This application claims the benefit of the filing date of Provisional U.S. Patent Application No. 60/273,164 entitled "Asset Utilization Expert in a Process Control Plant" filed on Mar. 1, 2001.
Claims

What is claimed is:
1. A method of analyzing a process, comprising the steps of: collecting process data within a process plant; transmitting the collected process data to a remote data processing facility; providing process plant configuration data relating to the setup of the process plant to the remote data processing facility; storing the process plant configuration data within a database at the remote data processing facility; analyzing the process data within the remote data processing facility to generate analysis data using the process plant configuration data and one of a plurality of asset or process analysis tools stored within a database of the remote data processing facility; and transmitting the analysis data to the process plant.

2. The method of claim 1, wherein the step of analyzing the process data within the remote data processing facility to generate analysis data using the process plant configuration data and one of the plurality of asset or process analysis tools includes the step of using one of a process control tool, a process monitoring tool, an equipment monitoring tool, a device monitoring tool, an index generation tool, a work order generation tool and a business tool.

3. The method of claim 1, wherein the step of transmitting the collected process data to the remote data processing facility includes the step of transmitting the collected process data via an open network.

4. The method of claim 3, wherein the step of transmitting the collected process data via the open network includes the step of transmitting the collected process data using the Internet.

5. The method of claim 1, wherein the step of analyzing the process data within the remote data processing facility to generate analysis data using the process plant configuration data and one of the plurality of asset or process analysis tools includes the step of using a model of a portion of the process plant.

6. The method of claim 5, wherein the step of using the model of the portion of the process plant includes the step of using one of a device model, a loop model and a unit model.

7. The method of claim 1, wherein the step of analyzing the process data within the remote data processing facility to generate analysis data using the process plant configuration data and one of the plurality of asset or process analysis tools includes the step of using an optimization tool.

8. The method of claim 1, wherein the step of transmitting the analysis data to the process plant includes the step of transmitting one of problem data, condition data, plant data, process data, device data and optimization data.

9. The method of claim 1, wherein the step of transmitting the analysis data to the process plant includes the step of transmitting one of a health index value, a performance index value, a utilization index value and a variability index value that is associated with a portion of the process plant.

10. The method of claim 1, wherein the step of collecting the process data within the process plant includes the step of collecting a predetermined set of process data for use by the plurality of asset or process analysis tools.

11. The method of claim 1, wherein the step of collecting the process data within the process plant includes the step of repeatedly collecting the process data at a predetermined rate.

12. The method of claim 1, further comprising the step of transmitting update information from the process plant to the remote data processing facility to update the one of the plurality of asset or process analysis tools.

13. The method of claim 12, wherein the step of transmitting the update information from the process plant to the remote data processing facility to update the one of the plurality of asset or process analysis tools includes the step of transmitting updated model information.

14. The method of claim 12, wherein the step of transmitting the update information from the process plant to the remote data processing facility to update the one of the plurality of asset or process analysis tools includes the step of using a communication format based on a markup language.

15. A system for analyzing process data, comprising: a server that has a processor and that is communicatively coupled to a remote process plant; a database that is communicatively coupled to the server; process plant configuration data relating to the setup of the remote process plant stored within the database; and a plurality of asset or process analysis tools stored within the database, wherein the processor is programmed to use the process plant configuration data and one of the plurality of asset or process analysis tools to analyze the process data received from the remote process plant to generate analysis data and to send the analysis data to the remote process plant.

16. The system of claim 15, wherein the server is communicatively coupled to the remote process plant via an open network.

17. The system of claim 16, wherein the open network is the Internet.

18. The system of claim 15, wherein the plurality of asset or process analysis tools includes one of a process control tool, a process monitoring tool, an equipment monitoring tool, a device monitoring tool, an index generation tool, a work order generation tool and an accounting tool.

19. The system of claim 15, wherein the one of the plurality of asset or process analysis tools includes a model of a portion of the remote process plant.

20. The system of claim 19, wherein the model of the portion of the remote process plant includes one of a device model, a loop model and a unit model.

21. The system of claim 15, wherein the one of the plurality of asset or process analysis tools includes an optimization tool.

22. The system of claim 15, wherein the analysis data includes one of problem data, condition data, plant data, process data, device data and optimization data.

23. The system of claim 15, wherein the analysis data includes one of a health index value, a performance index value, a utilization index value, and a variability index value that is associated with a portion of the remote process plant.

24. The system of claim 15, wherein the processor is further programmed to receive update information from the remote process plant for updating the one of the plurality of asset or process analysis tools.

25. The system of claim 24, wherein the update information includes updated model information.

26. The system of claim 25, wherein the processor is further programmed to send the analysis data to the process plant using a communication format based on a markup language.

27. A system that analyzes process data, comprising: a computer readable medium; and a first routine stored on the computer readable medium and adapted to be executed by a processor that receives process data from a remote process plant; a second routine stored on the computer readable medium and adapted to be executed by the processor that analyzes the process data received from the remote process plant using process plant configuration data relating to the setup of the remote process plant to generate analysis data; and a third routine stored on the computer readable medium and adapted to be executed by the processor that sends the analysis data to the remote process plant.

28. The system of claim 27, wherein the third routine is further adapted to send the analysis data to the remote process plant via the Internet.

29. The system of claim 27, wherein the plurality of asset or process analysis tools includes one of a process control tool, a process monitoring tool, an equipment monitoring tool, a device monitoring tool, an index generation tool, a work order generation tool and a business tool.

30. The system of claim 27, wherein the plurality of asset or process analysis tools includes an optimization tool.

31. The system of claim 27, wherein the analysis data includes one of a health index value, a performance index value, a utilization index value and a variability index value that is associated with a portion of the remote process plant.

32. A method of analyzing asset utilization, comprising the steps of: receiving process data from a remote process plant via the Internet; storing process plant configuration data relating to the setup of the remote process plant; analyzing the process data to generate analysis data using the process plant configuration data and one of a plurality of asset or process analysis tools stored within a database; and transmitting the analysis data to the remote process plant via the Internet.

33. The method of claim 32, wherein the step of analyzing the process data using the process plant configuration data and one of the plurality of asset or process analysis tools includes the step of using one of a process control tool, a process monitoring tool, an equipment monitoring tool, a device monitoring tool, an index generation tool, a work order generation tool and an accounting tool.

34. The method of claim 32, wherein the step of analyzing the process data using the process plant configuration data and one of the plurality of asset or process analysis tools includes the step of using a model of a portion of the remote process plant.

35. The method of claim 32, wherein the step of analyzing the process data using the process plant configuration data and one of the plurality of asset or process analysis tools includes the step of using an optimization tool.

36. The method of claim 32, wherein the step of transmitting the analysis data to the remote process plant includes the step of transmitting one of problem data, condition data, plant data, process data, device data and optimization data.

37. The method of claim 32, wherein the step of transmitting the analysis data to the remote process plant includes the step of transmitting one of a health index value, a performance index value, a utilization index value and a variability index value that is associated with a portion of the remote process plant.

38. A method of analyzing process control data, comprising the steps of: sending a first data template to a customer associated with a process plant; receiving a second data template that is formed using the first data template and process control information generated within the process plant; analyzing the process control information within the second data template to produce analysis results; and notifying the customer of the availability of the analysis results via an internet.

39. The method of claim 38, wherein the step of sending the first data template to the customer includes the step using the internet to send the first data template in response to a request from the customer received via a web site associated with an application service provider.

40. The method of claim 38, wherein the step of receiving the second data template that is formed using the first data template and process control information generated by the process plant associated with the customer includes the step of receiving process control information associated with a particular piece of equipment within the process plant.

41. The method of claim 38, wherein the step of analyzing the process control information within the second data template to produce the analysis results includes the step of using one of a data analysis tool, a model and an optimizer to analyze the process control information.

42. The method of claim 38, wherein the step of notifying the customer of the availability of the analysis results via the internet includes the step of using one of an electronic mail and a web page to notify the customer.

43. The method of claim 38, further comprising the step of storing the second data template and the analysis results in a customer database.

44. The method of claim 38, further comprising the steps of pre-processing the process control information using a data reconciliation routine and validating the analysis results prior to notifying the customer of the availability of the analysis results.

45. The method of claim 38, further comprising the step of displaying a web page to the customer containing a report based on the analysis results, wherein the report includes one of performance information, cost information and historical information associated with a particular piece of equipment within the process plant.

46. A system for use in analyzing process control data, the system comprising: a computer readable medium; a first routine stored on the computer readable medium and adapted to be executed by a processor that enables a customer to contract with an application service provider to analyze process control data for the purpose of providing data analysis services and results for at least a portion of a process plant; a second routine stored on the computer readable medium and adapted to be executed by the processor that designs a data model based on design data received from the customer and a model stored by the application service provider; and a third routine stored on the computer readable medium and adapted to be executed by the processor that processes the data model using process control data received from the customer and that provides analysis results to the customer via an internet communication link.

47. The system of claim 46, wherein the first routine is further adapted to download contract terms and conditions to the customer via the internet communication link.

48. The system of claim 46, wherein the first routine is further adapted to configure a customer account in response to receiving an executed contract from the customer.

49. The system of claim 46, wherein the second routine is further adapted to download design data requirements to the customer via the internet communication link.

50. The system of claim 46, wherein the second routine is further adapted to use the design data received from the customer to form a design data spreadsheet and to fit the design data into the model stored by the application service provider.

51. The system of claim 46, wherein the third routine is further adapted to download a process data template to the customer via the internet communication link.

52. The system of claim 46, wherein the third routine is further adapted to pre-process the process control data using a data reconciliation routine.

53. The system of claim 46, wherein the third routine is further adapted to use one of a data analysis tool and an optimizer to process the data model.

54. The system of claim 46, wherein the third routine is further adapted to store the analysis results in a customer database and to notify the customer via the internet communication link of the availability of the analysis results.

55. The system of claim 46, wherein the third routine is further adapted to display a web page to the customer containing a report based on the analysis results, wherein the report includes one of performance information, cost information and historical information associated with a particular piece of equipment within a process plant associated with the customer.

56. A system for use analyzing process control data, the system comprising: a server that is communicatively coupled to an internet communication link; and a processor communicatively coupled to the server and to a plurality of databases, wherein the processor is programmed to download a process control data template to a customer via the internet communication link and wherein the processor is further programmed to use one of an optimizer and a data analysis tool to analyze a populated design data template that is formed using the process control data template and process data generated within a process plant associated with the customer.

57. The system of claim 56, wherein the plurality of databases includes one of a data analysis tools database, a models database, an optimizers database, a web pages database and a customer database.

58. A method of maintaining a process control system, comprising the steps of: sending a first data template to a customer associated with a process plant; receiving a second data template that is formed using the first data template and process control information generated within the process plant; analyzing the process control information within the second data template to produce analysis results; and scheduling maintenance activities for the process plant based on the analysis results via an internet communication link.

59. The method of claim 58, wherein the step of scheduling maintenance activities for the process plant based on the analysis results via an internet communication link includes the step of scheduling a maintenance outage within the process plant based on the analysis results.

60. The method of claim 58, wherein the step of scheduling maintenance activities for the process plant based on the analysis results via an internet communication link includes the step of ordering a part from a supplier via the internet communication link based on the analysis results.
Description
FIELD OF THE INVENTION

The present invention relates generally to process control systems within process plants and, more particularly, to the use of asset or process analysis tools by a remote processing facility to analyze process control plant data.

DESCRIPTION OF THE RELATED ART

Process control systems, like those used in chemical, petroleum or other processes, typically include one or more centralized or decentralized process controllers communicatively coupled to at least one host or operator workstation and to one or more process control and instrumentation devices, such as field devices, via analog, digital or combined analog/digital buses. Field devices, which may be, for example valves, valve positioners, switches, transmitters, and sensors (e.g., temperature, pressure and flow rate sensors), perform functions within the process such as opening or closing valves and measuring process parameters. The process controller receives signals indicative of process measurements or process variables made by or associated with the field devices and/or other information pertaining to the field devices, uses this information to implement a control routine and then generates control signals which are sent over one or more of the buses to the field devices to control the operation of the process. Information from the field devices and the controller is typically made available to one or more applications executed by an operator workstation to enable an operator to perform desired functions with respect to the process, such as viewing the current state of the process, modifying the operation of the process, etc.

While a typical process control system has many process control and instrumentation devices, such as valves, transmitters, sensors, etc. connected to one or more process controllers which execute software that controls these devices during the operation of the process, there are many other supporting devices that are also necessary for or related to process operation. These additional devices include, for example, power supply equipment, power generation and distribution equipment, rotating equipment such as turbines, etc., all of which are typically distributed throughout a plant. While this additional equipment does not necessarily create or use process variables and in many instances is not controlled or even coupled to a process controller for the purpose of affecting the process operation, this equipment is nevertheless important to and is ultimately necessary for proper operation of the process. In the past, process controllers were typically not aware of these other devices or the process controllers simply assumed that these devices were operating properly when performing process control.

Still further, many process plants have other computers that execute applications related to business functions or maintenance functions. For example, some plants include computers that execute applications associated with ordering raw materials, replacement parts or devices for the plant, applications related to forecasting sales and production needs, etc. Likewise, many process plants, and especially those that use smart field devices, include applications which are used to help monitor and maintain the devices within the plant, regardless of whether these devices are process control and instrumentation devices or are other types of devices. For example, the Asset Management Solutions (AMS) application sold by Fisher-Rosemount Systems, Inc. enables communication with and stores data pertaining to field devices to ascertain and track the operating state of the field devices. An example of such a system is disclosed in U.S. Pat. No. 5,960,214 entitled "Integrated Communication Network for use in a Field Device Management System." In some instances, the AMS application may be used to communicate with devices to change parameters within the device, to cause the device to run applications on itself, such as self calibration routines or self diagnostic routines, to obtain information about the status or health of the device, etc. This information may be stored and used by a maintenance person to monitor and maintain these devices. Likewise, there are other types of applications that are used to monitor other types of devices, such as rotating equipment and power generation and power supply devices. These other applications are typically available to the maintenance persons and are used to monitor and maintain the devices within a process plant.

However, in the typical plant or process, the functions associated with the process control activities, the device and equipment maintenance and monitoring activities, and the business activities are separated, both in the location in which these activities take place and in the personnel who typically perform these activities. Furthermore, the different people involved in these different functions generally use different tools, such as different applications run on different computers to perform the different functions. In many instances, these different tools collect or use different types of data associated with or collected from the different devices within the process and are set up differently to collect the data they need. For example, process control operators who generally oversee the day to day operation of the process and who are primarily responsible for assuring the quality and continuity of the process operation typically affect the process by setting and changing set points within the process, tuning loops of the process, scheduling process operations such as batch operations, etc. These process control operators may use available tools for diagnosing and correcting process control problems within a process control system, including, for example, auto-tuners, loop analyzers, neural network systems, etc. Process control operators also receive process variable information from the process via one or more process controllers which provide information to the operators about the operation of the process, including alarms generated within the process. This information may be provided to the process control operator via a standard user interface.

Still further, it is currently known to provide an expert engine that uses process control variables and limited information about the operating condition of the control routines or function blocks or modules associated with process control routines to detect poorly operating loops and to provide information to an operator about suggested courses of action to correct the problem. Such an expert engine is disclosed in U.S. patent application Ser. No. 09/256,585 entitled "Diagnostics in a Process Control System," which was filed on Feb. 22, 1999 and in U.S. patent application Ser. No. 09/499,445 entitled "Diagnostic Expert in a Process Control System," which was filed on Feb. 7, 2000, both of which are hereby expressly incorporated by reference herein. Likewise, it is known to run control optimizers, such as real time optimizers, within a plant to optimize the control activities of the process plant. Such optimizers typically use complex models of the plant to predict how inputs may be changed to optimize operation of the plant with respect to some desired optimization variable such as, for example, profit.

On the other hand, maintenance personnel who are primarily responsible for assuring that the actual equipment within the process is operating efficiently and for repairing and replacing malfunctioning equipment, use tools such as maintenance interfaces, the AMS application discussed above, as well and many other diagnostic tools that provide information about operating states of the devices within the process. Maintenance persons also schedule maintenance activities that may require shut down of portions of the plant. For many newer types of process devices and equipment, which are generally smart field devices, the devices themselves may include detection and diagnostic tools that automatically sense problems with the operation of the device and automatically report these problems to a maintenance person via a standard maintenance interface. For example, the AMS software reports device status and diagnostic information to the maintenance person and provides communication and other tools that enable the maintenance person to determine what is happening in devices and to access device information provided by devices. Typically, maintenance interfaces and maintenance personnel are located apart from process control operators, although this is not always the case. For example, in some process plants, process control operators may perform the duties of maintenance persons or vice versa, or the different people responsible for these functions may use the same interface.

Still further, persons responsible for applications used for business applications, such as ordering parts, supplies, raw materials, etc., making strategic business decisions such as choosing which products to manufacture, what variables to optimize within the plant, etc. are typically located in offices of the plant that are remote from both the process control interfaces and the maintenance interfaces. Likewise, managers or other persons may want to have access to certain information within the process plant from remote locations or from other computer systems associated with the process plant for use in overseeing the plant operation and in making long term strategic decisions.

Because, substantially different applications are used to perform the different functions within a plant, e.g., process control operations, maintenance operations and business operations are separated, the different applications used for these different tasks are not integrated and, thus, do not share data or information. In fact, many plants only include some, but not all, of these different types of applications. Furthermore, even if all of the applications are located within a plant, because different personnel use these different applications and analysis tools and because these tools are generally located at different hardware locations within the plant, there is little if any flow of information from one functional area of the plant to another, even when this information may be useful to other functions within the plant. For example, a tool, such as a rotating equipment data analysis tool, may be used by a maintenance person to detect a poorly functioning power generator or piece of rotating equipment (based on non-process variable type data). This tool may detect a problem and alert the maintenance person that the device needs to be calibrated, repaired or replaced. However, the process control operator (either a human or a software expert) does not have the benefit of this information, even though the poorly operating device may be causing a problem that is affecting a loop or some other component which is being monitored by the process control operation. Likewise, the business person is not aware of this fact, even though the malfunctioning device may be critical to and may be preventing optimization of the plant in a manner that the business person desires. Because the process control expert is unaware of a device problem that may be ultimately causing poor performance of a loop or unit in the process control system and because the process control operator or expert assumes that this equipment is operating perfectly, the process control expert may misdiagnose the problem it detects within the process control loop or may try to apply a tool, such as a loop tuner, which could never correct the problem. Likewise, the business person may make a business decision to run the plant in a manner that will not achieve the desired business effects (such as optimizing profits) because of the malfunctioning device.

Due to the abundance of data analysis and other detection and diagnostic tools available in the process control environment, there is a lot of information pertaining to the health and performance of devices available to the maintenance person that could be helpful to process operators and business persons. Similarly, there is a lot of information available to process operators about the current operational status of the process control loops and other routines that may be helpful to maintenance persons or to business persons. Likewise, there is information generated by or used in the course of performing the business functions that could be helpful to maintenance persons or process control operators in optimizing the operation of the process. However, in the past, because these functions were separated the information generated or collected in one functional area was not used at all, or not used very well, in other functional areas, resulting in a sub-optimal use of the assets within process plants.

Furthermore, legal pressures such as, for example, increased environmental regulation and increased competition has caused improved efficiency of the process control activities within a plant to become a significant source of profit improvement. While a variety of data analysis tools such as optimization software, maintenance software, and a variety of other well known asset management methods, tools or software such as those described above are widely used within process control plants, supporting such methods, tools and software often result in a substantial cost to the plant owner.

More specifically, the efficient operation of a plant depends strongly on the condition of the equipment within the plant and the timing of maintenance on that equipment. Traditionally, equipment performance monitoring tools such as, for example, input/output algorithms, models, etc. have been used to determine how efficiently a plant is running and/or whether a more cost effective process can be achieved through changes in maintenance procedures, replacement of worn equipment, modification of equipment, etc. Unfortunately, equipment performance monitoring requires significant expenditures for hardware and software (e.g., data analysis tools) and also typically requires skilled technicians and other specialists to support and oversee the daily performance monitoring activities. Many plant owners and operators have recognized that the high costs associated with equipment performance monitoring activities has become an important area for competitive cost reductions, particularly in the case of smaller plant operations for which economies of scale dictate greater focus on core competencies.

SUMMARY OF THE INVENTION

In accordance with one aspect of the invention, a system and method for analyzing a process collects process data within a process control plant and transmits the collected process data to a remote data processing facility. The remote data processing facility may analyze the process data to generate analysis data using one of a plurality asset or process analysis tools stored within a database of the remote data processing facility. The analysis data may then be transmitted to the process control plant.

The system and method may analyze the process data within the remote data processing facility using one of a process control tool, a process monitoring tool, an equipment monitoring tool, a device monitoring tool, an index generation tool, a work order generation tool and an accounting tool. Additionally, the system and method may transmit the collected process data to the remote data processing facility via an open network.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary block diagram of a process control plant having an asset utilization expert configured to receive and coordinate data transfer between many functional areas of the plant;

FIG. 2 is an exemplary data and information flow diagram with respect to the asset utilization expert within the plant of FIG. 1;

FIG. 3 is an exemplary block diagram of a model used to simulate the operation of an area within a plant;

FIG. 4 is an exemplary block diagram of a model used to simulate the operation of a unit within the area model of FIG. 3;

FIG. 5 is an exemplary two-dimensional performance monitoring diagram;

FIG. 6 is a graph illustrating an exemplary fiducial line chosen for use in a furnace and a coking rate based on this fiducial line;

FIG. 7 is a graph illustrating the development of a new coking rate based on the fiducial line of FIG. 6;

FIG. 8 is an exemplary depiction of a display representing a unit within a process control system that may be displayed by a graphical user interface;

FIG. 9 is an exemplary table that illustrates one manner in which indexes may be generated for different levels of a system hierarchy;

FIG. 10 is an exemplary chart depicting one manner in which a performance index for a unit may be calculated;

FIG. 11 is an exemplary table that illustrates one manner in which index values may be used to calculate a new index value as a weighted average of the index values;

FIG. 12 is an exemplary table that illustrates one manner in which a variability index may be calculated for a unit;

FIG. 13 is an exemplary display that may be provided by a graphical user interface in response to an abnormal variability index;

FIG. 14 is an exemplary display of the data used to generate a variability index;

FIG. 15 is an exemplary graphical depiction of a display that may be provided by a graphical user interface to enable a user to monitor indexes associated with a portion of a plant;

FIG. 16 is an exemplary graphical display that may be provided by a graphical user interface to enable a user to analyze the operational status and performance of a process area within a plant;

FIG. 17 is an exemplary depiction of a display that may be provided by a graphical user interface to enable a user to view audit trail information;

FIG. 18 is an exemplary depiction of a display that may be provided by a graphical user interface to enable a user to perform a more detailed analysis of data used to generate one or more indexes for a device;

FIG. 19 is an exemplary depiction of a display that may be provided by a graphical user interface to enable a user to graphically view or monitor a performance characteristic of a device;

FIG. 20 is yet another exemplary depiction of a display that may be provided by a graphical user interface to enable a user to quickly investigate information within a plant;

FIGS. 21-23 are exemplary pop-up windows that may be displayed by a graphical user interface to provide device status information;

FIG. 24 is an exemplary display that may be provided by a graphical user interface to give detailed help information to a user;

FIG. 25 is an exemplary depiction of a display that may be provided by a graphical user interface to enable a user to diagnose loop-related problems;

FIG. 26 is yet another exemplary depiction of a display that may be provided by a graphical user interface that enables a user to analyze the performance and/or status of one or more process control loops;

FIG. 27 is still another exemplary depiction of a display that may be provided by a graphical user interface to enable a user to track or generate work orders;

FIGS. 28-31 are exemplary displays depicting spectral plots of vibration of an element within a rotary device;

FIG. 32 is a schematic block diagram of a system that enables one or more independently operable process control plants to remotely access models, optimizers and other data analysis tools;

FIG. 33 is a more detailed schematic block diagram of the application service provider shown in FIG. 32;

FIG. 34 is an exemplary flow diagram that illustrates a routine that may be used by a process control plant to establish a business relationship with the application service provider shown in FIGS. 32 and 33;

FIG. 35 is an exemplary flow diagram of a routine that may be used by the application service provider shown in FIGS. 32 and 33 to design a data model for a plant;

FIG. 36 is an exemplary flow diagram of a routine that may be used by the application service provider shown in FIGS. 32 and 33 to process and/or analyze process control data; and

FIGS. 37-41 are exemplary web pages containing analysis results that may be displayed to a customer using the system shown FIG. 32.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring now to FIG. 1, a process control plant 10 includes a number of business and other computer systems interconnected with a number of control and maintenance systems by one or more communication networks. The process control plant 10
includes one or more process control systems 12 and 14. The process control system 12 may be a traditional process control system such as a PROVOX or RS3 system or any other DCS that includes an operator interface 12A coupled to a controller 12B and to input/output (I/O) cards 12C which, in turn, are coupled to various field devices such as analog and Highway Addressable Remote Transmitter (HART) field devices 15. The process control system 14, which may be a distributed process control system, includes one or more operator interfaces 14A coupled to one or more distributed controllers 14B via a bus, such as an Ethernet bus. The controllers 14B may be, for example, DeltaV.TM. controllers sold by Fisher-Rosemount Systems, Inc. of Austin, Tex. or any other desired type of controllers. The controllers 14B are connected via I/O devices to one or more field devices 16, such as for example, HART or Fieldbus field devices or any other smart or non-smart field devices including, for example, field devices that use any of the PROFIBUS.RTM., WORLDFIP.RTM., Device-Net.RTM., AS-Interface and CAN protocols. As is known, the field devices 16 may provide analog or digital information to the controllers 14B related to process variables as well as to other device information. The operator interfaces 14A may store and execute tools available to the process control operator for controlling the operation of the process including, for example, control optimizers, diagnostic experts, neural networks, tuners, etc.

Still further, maintenance systems, such as computers executing the AMS application or any other device monitoring and communication applications may be connected to the process control systems 12 and 14 or to the individual devices therein to perform maintenance and monitoring activities. For example, a maintenance computer 18 may be connected to the controller 12B and/or to the devices 15 via any desired communication lines or networks (including wireless or handheld device networks) to communicate with and, in some instances, reconfigure or perform other maintenance activities on the devices 15. Similarly, maintenance applications such as the AMS application may be installed in and executed by one or more of the user interfaces 14A associated with the distributed process control system 14 to perform maintenance and monitoring functions, including data collection related to the operating status of the devices 16.

The process control plant 10 also includes rotating equipment 20, such as turbines, motors, etc. that are connected to a maintenance computer 22 via a permanent or temporary communication link (such as a bus, a wireless communication system or hand held devices that are connected to the equipment 20 to take readings and which are then removed). The maintenance computer 22 may store and execute known monitoring and diagnostic applications 23 provided by, for example, CSi Systems or other any other known applications used to diagnose, monitor and optimize the operating state of the rotating equipment 20. Maintenance personnel usually use the diagnostic applications 23 to maintain and oversee the performance of the rotating equipment 20 to determine problems with the rotating equipment 20 and to determine when and if the rotating equipment 20 must be repaired or replaced.

Similarly, a power generation and distribution system 24 having power generating and distribution equipment 25 associated with the plant 10 is connected via, for example, a bus, to another computer 26 that runs and oversees the operation of the power generating and distribution equipment 25. The computer 26 may execute known power control and diagnostics applications 27 such as those provided by, for example, Liebert, ASCO or any other company to control and maintain the power generation and distribution equipment 25.

In the past, the various process control systems 12 and 14 and the power generating and maintenance systems 22 and 26 have not been interconnected in a manner that enables these systems to share data generated in or collected by each of these systems in a useful manner. As a result, each of the different functions such as process control functions, power generation functions and rotating equipment functions have operated on the assumption that the other equipment within the plant, which may be affected by or have an affect on that particular function, is operating perfectly which, of course, is almost never the case. However, because the functions are so different and because the equipment and personnel that oversee these functions are different, there has been little or no meaningful data sharing between the different functional systems within the plant 10.

To overcome this problem, a computer system 30 is provided that is communicatively connected to the computers or interfaces associated with the various functional systems within the plant 10, including the process control functions 12 and 14, the maintenance functions, such as those implemented in the computers 18, 14A, 22 and 26 and the business functions. In particular, the computer system 30 is communicatively connected to the traditional process control system 12 and to the maintenance interface 18 associated with the control system 12, is connected to the process control and/or maintenance interfaces 14A of the distributed process control system 14, is connected to the rotating equipment maintenance computer 22 and the power generation and distribution computer 26, all via a bus 32. The bus 32 may use any desired or suitable local area network (LAN) or wide area network (WAN) protocol to provide communications.

As illustrated in FIG. 1, the computer 30 is also connected via the same or a different network bus 32 to business system computers and maintenance planning computers 35 and 36, which may execute, for example, enterprise resource planning (ERP), material resource planning (MRP), accounting, production and customer ordering systems, maintenance planning systems or any other desired business applications such as parts, supplies and raw materials ordering applications, production scheduling applications, etc. The computer 30 may also be connected via, for example, the bus 32, to a plantwide LAN 37, a corporate WAN 38 as well as to a computer system 40 that enables remote monitoring of or communication with the plant 10 from remote locations.

In one embodiment, the communications over the bus 32 occur using the XML protocol. In that case, data from each of the computers 12A, 18, 14A, 22, 26, 35, 36, etc. is wrapped in an XML wrapper and is sent to an XML data server that may be located in, for example, the computer 30. Because XML is a descriptive language, the server can process any type of data. At the server, if necessary, the data is encapsulated with a new XML wrapper, i.e., this data is mapped from one XML schema to one or more other XML schemas that are created for each of the receiving applications. Thus, each data originator can wrap its data using a schema understood or convenient for that device or application, and each receiving application can receive the data in a different schema used for or understood by the receiving application. The XML data server is configured to map one schema to another schema depending on the source and destination(s) of the data. If desired, the server may also perform certain data processing functions or other functions based on the receipt of data. The mapping and processing function rules are set up and stored in the XML data server prior to operation of the system described herein. In this manner, data may be sent from any one application to one or more other applications.

Generally speaking, the computer 30 stores and executes an asset utilization expert 50 that collects data and other information generated by the process control systems 12 and 14, the maintenance systems 18, 22 and 26 and the business systems 35
and 36 as well as information generated by data analysis tools executed in each of these systems. The asset utilization expert 50 may be based on, for example, the OZ expert system currently provided by NEXUS. However, the asset utilization expert 50
may be any other desired type of expert system including, for example, any type of data mining system. Importantly, the asset utilization expert 50 operates as a data and information clearinghouse in the process plant 10 and is able to coordinate the distribution of data or information from one functional area, such as the maintenance area, to other functional areas, such as the process control or the business functional areas. The asset utilization expert 50 may also use the collected data to generate new information or data that can be distributed to one or more of the computer systems associated with the different functions within the plant 10. Still further, the asset utilization expert 50 may execute or oversee the execution of other applications that use the collected data to generate new types of data to be used within the process control plant 10.

In particular, the asset utilization expert 50 may include or execute index generation software 51 that creates indexes associated with devices, like process control and instrumentation devices, power generation devices, rotating equipment, units, areas, etc, or indexes that are associated with process control entities, like loops, etc. within the plant 10. These indexes may then be provided to the process control applications to help optimize process control and may be provided to the business software or business applications to provide the business persons more complete or understandable information associated with the operation of the plant 10. The asset utilization expert 50 can also provide maintenance data (such as device status information) and business data (such as data associated with scheduled orders, timeframes, etc.) to a control expert 52 associated with, for example, the process control system 14 to help an operator perform control activities such as optimizing control. The control expert 52 may be located in, for example, the user interface 14A or any other computer associated with the control system 14 or within the computer 30, if desired.

In one embodiment, the control expert 52 may be, for example, the control expert described in U.S. patent application Ser. Nos. 09/256,585 and 09/499,445 identified above. However, these control experts may additionally incorporate and use data related to the status of devices or other hardware within the process control plant 10 in the decision making performed by the control experts. In particular, in the past, the software control experts generally only used process variable data and some limited device status data to make decisions or recommendations to the process operator. With the communication provided by the asset utilization expert 50, especially that related to device status information such as that provided by the computer systems 18, 14A, 22 and 26 and the data analysis tools implemented thereon, the control expert 52 can receive and incorporate device status information such as health, performance, utilization and variability information into its decision making along with process variable information.

Additionally, the asset utilization expert 50 can provide information pertaining to states of devices and the operation of the control activities within the plant 10 to the business systems 35 and 36 where, for example, a work order generation application or program 54 can automatically generate work orders and order parts based on detected problems within the plant 10 or where supplies can be ordered based on work being performed. Similarly, changes in the control system detected by the asset utilization expert 50 may cause the business systems 35 or 36 to run applications that perform scheduling and supply orders using, for example, the program 54. In the same manner, changes in customer orders can be entered into the business systems
35 or 36 and this data can be sent to the asset utilization expert 50 and sent to the control routines or control expert 52 to cause changes in the control to, for example, to begin making the newly ordered products or to implement the changes made in the business systems 35 and 36. Of course, if desired, each computer system connected to the bus 32 may have an application therein that functions to obtain the appropriate data from the other applications within the computer and sends this data to, for example, the asset utilization expert 50.

Additionally, the asset utilization expert 50 can send information to one or more optimizers 55 within the plant 10. For example, a control optimizer 55 can be located in the computer 14A and can run one or more control optimization routines
55A, 55B, etc. Additionally or alternatively, optimizer routines 55 could be stored in and executed by the computer 30 or any other computer, and the data necessary therefor could be sent by the asset utilization expert 50. If desired, the plant 10 may also include models 56 that model certain aspects of the plant 10 and these models 56 can be executed by the asset utilization expert 50 or a control or other expert such as the control expert 52 to perform modeling functions, the purpose of which will be described in more detail herein. Generally speaking, however, the models 56 can be used to determine device, area, unit, loop, etc. parameters, to detect faulty sensors or other faulty equipment, as part of optimizer routines 55, to generate indexes such as performance and utilization indexes for use in the plant 10, to perform performance or condition monitoring, as well as for many other uses. The models 56 may be models such as those created by and sold by MDC Technology located in Teeside, England or may be any other desired types of models. There are, of course, many other applications that can be provided within the plant 10 and that can use the data from the asset utilization expert 50 and the system described herein is not limited to the applications specifically mentioned herein. Overall, however, the asset utilization expert 50 helps to optimize the use of all of the assets within the plant 10 by enabling the sharing of data and coordination of assets between all of the functional areas of the plant 10.

Also, generally speaking, one or more user interface routines 58 can be stored in and executed by one or more of the computers within the plant 10. For example, the computer 30, the user interface 14A, the business system computer 35 or any other computer may run a user interface routine 58. Each of the user interface routines 58 can receive or subscribe to information from the asset utilization expert 50 and either the same or different sets of data may be sent to each of the user interface routines 58. Any one of the user interface routines 58 can provide different types of information using different screens to different users. For example, one of the user interface routines 58 may provide a screen or set of screens to a control operator or to a business person to enable that person to set constraints or to choose optimization variables for use in a standard control routine or in a control optimizer routine. The user interface routine 58 may provide a control guidance tool that enables a user to view the indexes created by the index generation software 51 in some coordinated manner. This operator guidance tool may also enable the operator or any other person to obtain information about the states of devices, control loops, units, etc. and to easily see the information related to the problems with these entities, as that information has been detected by other software within the process plant 10. The user interface routine 58 may also provide performance monitoring screens using performance monitoring data provided by or generated by the tools 23 and 27, the maintenance programs such as the AMS application or any other maintenance programs, or as generated by the models in conjunction with the asset utilization expert 50. Of course, the user interface routine 58 may provide any user access to and enable the user to change preferences or other variables used in any or all functional areas of the plant 10.

FIG. 2 is a data flow diagram illustrating some of the data flow between the asset utilization expert 50 and other computer tools or applications within the process plant 10 is provided. In particular, the asset utilization expert 50 may receive information from numerous data collectors or data sources such as multiplexers, transmitters, sensors, hand held devices, control systems, radio frequency (RF) transceivers, on-line control systems, web servers, data historians, control modules or other control applications within the process control plant 10, interfaces such as user interfaces and I/O interfaces as well as data servers such as buses (e.g., Fieldbus, HART and Ethernet buses), valves, transceivers, sensors, servers and controllers and other plant assets such as process instrumentation, rotating equipment, electrical equipment, power generation equipment, etc. This data can take on any desired form based on how the data is generated or used by other functional systems. Still further, this data may be sent to the asset utilization expert 50 using any desired or appropriate data communication protocol and communication hardware such as the XML protocol discussed above. Generally speaking, however, the plant 10 is configured so that the asset utilization expert 50 automatically receives specific kinds of data from one or more of the data sources and so that the asset utilization expert 50 can take predetermined actions with respect to that data.

Also, the asset utilization expert 50 receives information from (and may execute) data analysis tools such as typical maintenance data analysis tools that are currently provided today, performance tracking tools, such as those associated with devices, as well as performance tracking tools for process control systems like that described in U.S. patent application Ser. Nos. 09/256,585 and 09/499,445 identified above. The data analysis tools may also include, for example, a root cause application which detects root causes of certain types of problems, event detection such as that described in U.S. Pat. No. 6,017,143, regulatory loop diagnostics such as that disclosed in U.S. patent application Ser. No. 09/303,869 (filed May 3,
1999), which is hereby expressly incorporated by reference herein, impulse line plugging detection applications, such as that described in U.S. patent application Ser. No. 09/257,896 (filed Feb. 25, 1999), which is hereby expressly incorporated by reference herein, other plugged line detection applications, device status applications, device configuration applications and maintenance applications, device storage, historian and information display tools, such as AMS, Explorer applications and audit trail applications. Still further, the expert 50 can receive data and any information from process control data analysis tools such as the advanced control expert 52, model predictive control process routines such as those described in U.S. patent application Ser. Nos. 09/593,327 (filed Jun. 14, 2000) and 09/412,078 (filed Oct. 4, 1999), which are hereby expressly incorporated by reference herein, tuning routines, fuzzy logic control routines and neural network control routines, as well as from virtual sensors such as that described in U.S. Pat. No. 5,680,409, which may be provided within the process control system 10. Still further, the asset utilization expert 50 may receive information from data analysis tools related to rotating equipment such as on-line vibration, RF wireless sensors and hand-held data collection units, oil analysis associated with rotating equipment, thermography, ultra-sonic systems and laser alignment and balancing systems, all of which may be related to detecting problems or the status of rotating equipment within the process control plant 10. These tools are currently known in the art and so will not be described further herein. Still further, the asset utilization expert 50 may receive data related to power management and power equipment and supplies such as the applications 23 and 27 of FIG. 1, which may include any desired power management and power equipment monitoring and analysis tools.

In one embodiment, the asset utilization expert 50 executes or oversees the execution of mathematical software models 56 of some or all of the equipment within the plant 10, such as device models, loops models, unit models, area models, etc., which are run by, for example, the computer 30 or any other desired computer within process plant 10. The asset utilization expert 50 may use the data developed by or associated with these models for a number of reasons. Some of this data (or the models themselves) may be used to provide virtual sensors within the plant 10. Some of this data, or the models themselves, may be used to implement predictive control or real time optimal control within the plant 10. Some of the data generated by the models 56 may be used by the index generation routine 51 to generate indexes which are used in other applications, such as business and process control applications. The use of the models 56 for these and other purposes will be described in more detail below.

The asset utilization expert 50 receives data as it is generated or at certain periodic times over, for example, the bus 32 or other any communication network within the process control plant 10. Thereafter, periodically or as needed, the asset utilization expert 50 redistributes the data to other applications or uses that data to generate and provide other information useful in different aspects of the control or operation of the process plant 10 to other function systems within the plant 10. In particular, the asset utilization expert 50 may supply data to cause the index generation routine 51 to create a series of composite indexes such as a performance index, a utilization index, a health index and a variability index associated with one or more of the devices, units, loops, areas, or other entities within the process control plant 10. The generation and use of these indexes will also be discussed in more detail herein.

The asset utilization expert 50 may also provide data to and receive data from control routines 62 that may be located in process controllers or interfaces associated with those controllers, optimizers 55, business applications 63, maintenance applications 66, etc.

Furthermore, a control expert 65 (which may include a predictive process controller), that in the past simply assumed the devices it was controlling either worked properly or not at all, can receive information from the asset utilization expert
50 related to the status or health of the devices it is controlling, such as the utilization, variability, health or performance indexes mentioned above or other information related to the operating status of devices, loops, etc. that can be taken into account when trying to control a process. The predictive controller 65, as well as the optimizers 55 may provide additional information and data to user interface routines 58. The predictive controller 65 or optimizer 55 may use the status information pertaining to actual current status of the devices in the network, as well as take into account goals and future needs such as those identified by business solution software provided from the asset utilization expert 50 as defined by, for example, business applications 63, to optimize control based on predictions within the control system.

Still further, the asset utilization expert 50 may provide data to and receive data from enterprise resource planning tools such as those typically used in business solutions or business computers 35 and 36. These applications may include production planning tools which control production planning, material resource planning, the work order generation tool 54, which automatically generates part orders, work orders, or supply orders for use in the business applications, etc. Of course, the part order, work order and supply order generation may be completed automatically based on information from the asset utilization expert 50, which decreases the time required to recognize that an asset needs to be fixed as well as the time it takes to receive the parts necessary to provide corrective action with respect to maintenance issues.

The asset utilization expert 50 may also provide information to the maintenance system applications 66, which not only alert maintenance people to problems immediately, but also take corrective measures such as ordering parts, etc. that are needed to correct a problem. Still farther, new models 68 may be generated using types of information that are available to the asset utilization expert 50 but that were previously unavailable to any single system. Of course, it will be understood from FIG. 2 that the asset utilization expert 50 receives information or data from the data models and the analysis tools and also receives information from enterprise resource tools, maintenance tools and process control tools.

Moreover, one or more coordinated user interface routines 58 may communicate with the asset utilization expert 50 as well as any other applications within the plant 10 to provide help and visualization to operators, maintenance persons, business persons, etc. The operators and other users may use the coordinated user interface routines 58 to perform or to implement predictive control, change settings of the plant 10, view help within the plant 10, or perform any other activities related to the information provided by the asset utilization expert 50. As discussed above, the user interface routines 58 may include an operator guidance tool that receives information from the predictive controller 65 as well as information related to the indexes, which can be used by an operator or other user to help perform many functions such as viewing the status of a process or devices within the process, to guide the predictive controller 65 or to perform predictive or optimized control. Still further, the user interface routines 58 may be used to view data or to obtain data from any of the tools in the other parts of the process control plant 10 via, for example, the asset utilization expert 50. For example, managers may want to know what is happening in the process or may need high level information related to the process plant 10 to make strategic plans.

As mentioned above, the asset utilization expert 50 can execute or oversee the execution of one or more mathematical or software models 56 that model the operation of a particular plant or entities within the plant, such as devices, units, loops, areas, etc. These models may be hardware models or they may be process control models. In one embodiment, to generate these models, a modeling expert divides the plant into component hardware and/or process control parts and provides a model for the different component parts at any desired level of abstraction. For example, the model for a plant is implemented in software and is made up of or may include a set of hierarchically related, interconnected models for the different areas of the plant. Similarly, the model for any plant area may be made up of individual models for the different units within the plant with interconnections between the inputs and outputs of these units. Likewise, units may be made up of interconnected device models, and so on. Of course, area models may have device models interconnected with unit models, loop models, etc. In this example model hierarchy, the inputs and outputs of models for the lower level entities, such as devices, may be interconnected to produce models for higher level entities, such as units, the inputs and outputs of which may be interconnected to create still higher level models, such as area models, and so on. The way in which the different models are combined or interconnected will, of course depend on the plant being modeled. While a single, complete mathematical model for the whole plant could be used, providing different and independent component models for different portions of or entities within the plant, such as areas, units, loops, devices, etc. and interconnecting these different models to form larger models may be useful for a number of reasons. Furthermore, it is desirable to use component models that can be run independently of one another as well as together with other component models as part of a larger model.

While highly mathematically accurate or theoretical models (such as third or fourth order models) may be used for the entire plant or for any or all of the component models, the individual models need not necessarily be as mathematically accurate as possible and could be, for example, first or second order models or other types of models. These simpler models can generally be executed more quickly in software and can be made more accurate by matching the inputs and outputs of the models with actual measurements of inputs and outputs made within the plant in a manner described herein. In other words, the individual models may be tuned or tweaked to accurately model the plant or the entities within the plant based on actual feedback from the plant.

The use of hierarchical software models will now be described in connection with FIGS. 3 and 4. FIG. 3 illustrates models for multiple areas 80, 81 and 82 within a refining plant. As illustrated in FIG. 3, the area model 82 includes a component model of a raw material source 84 that feeds raw material such as crude oil to a pre-processor model 88. The pre-processor model 88 provides some refining to the raw material and provides an output, such as crude oil, to a distillation process 90 for further refining. The distillation process 90 outputs C.sub.2 H.sub.4, usually a desired product, and C.sub.2 H.sub.6 which, generally speaking, is a waste product. The C.sub.2 H.sub.6 is fed back to a C.sub.2 cracker 92 which provides its output to the pre-processor 88 for further processing. The feedback from the distillation process 90 through the C.sub.2 cracker 92 is a recycling process. Thus, the model for the area 82 may include separate models for the raw material source 84, the pre-processor 88, the distillation process 90 and the C.sub.2 cracker 92 having inputs and outputs interconnected as illustrated in FIG. 3. That is, each component model may be tied to the inputs and outputs of other component models in the manner illustrated in FIG. 3 to form the model for the area 82. Of course, the models for the other areas 80 and 81 could have other component models having interconnected inputs and outputs.

Referring now to FIG. 4, the component model for the distillation process 90 is illustrated in more detail and includes a distillation column 100 having a top portion 100T and a bottom portion 100B. The input 103 to the distillation column 100
is an indication of pressure and temperature which may be tied to the output of the model for the pre-processor 88 shown in FIG. 3. However, this input could be set by an operator or be set based on actual measured inputs or variables within the plant
10. Generally speaking, the distillation column 100 includes a number of plates disposed therein and fluid moves between the plates during the distillation process. C.sub.2 H.sub.4 is produced out of the top 100T of the column 100 and a reflux drum 102
feeds back some of this material to the top 100T of the column 100. C.sub.2 H.sub.6 generally comes out of the bottom of the column 100 and a reboiler 104 pumps polypropylene into the bottom 100B of the column 100 to aid in the distillation process. Of course, if desired, the model for the distillation process 90 may be made up of component models for the distillation column 100, the reflux drum 102 and the reboiler 104, etc. and the inputs and outputs of these models may be connected as illustrated in FIG. 4 to form the component model for the distillation process 90.

As noted above, the component model for the distillation process 90 may be executed as part of a model for the area 82 or may be executed separately and apart from any other models. In particular, the input 103 to the distillation column 100
and/or the outputs C.sub.2 H.sub.4 and C.sub.2 H.sub.6 can actually be measured and these measurements may be used within the model of the distillation process 90 in a number of ways as described below. In one embodiment, the inputs and outputs of the model of the distillation process 90 may be measured and used to determine other factors or parameters associated with the model of the distillation process 90 (such as the distillation column efficiency, etc.) to cause the model of the distillation process 90 to more accurately match the operation of the actual distillation column within the plant 10. The model of the distillation process 90 may then be used with the calculated parameters as part of a larger model, such as an area or plant model. Alternatively or additionally, the model of the distillation process 90 with the calculated parameters may be used to determine virtual sensor measurements or to determine if actual sensor measurements within the plant 10 are in error. The model of the distillation process 90 with the determined parameters may also be used to perform control or asset utilization optimization studies, etc. Still further, component models may be used to detect and isolate developing problems in the plant 10 or to see how changes to the plant 10 might affect the selection of optimization parameters for the plant 10.

If desired, any particular model or component model may be executed to determine the values of the parameters associated with that model. Some or all of these parameters such as efficiency parameters may mean something to an engineer within the context of the model but are generally unmeasurable within the plant 10. More particularly, a component model may be generally mathematically described by the equation Y=F(X, P), wherein the outputs Y of the model are a function of the inputs X and a set of model parameters P. In the example of the distillation column model of the distillation process 90 of FIG. 4, an expert system may periodically collect data (e.g., every hour, every ten minutes, every minute, etc.) from the actual plant indicative of the actual inputs X to and the outputs Y from the entity to which the model pertains. Then, a regression analysis such as a maximum likelihood, least squares or any other regression analysis may be periodically performed using the model and multiple sets of the measured inputs and outputs to determine a best fit for the unknown model parameters P based on the multiple sets of measured data. In this manner, the model parameters P for any particular model may be determined using actual or measured inputs and outputs to reconcile the model with the entity being modeled. Of course, this process can be performed for any and all component models used within the plant 10 and can be performed using any appropriate number of measured inputs and outputs. Preferably, the asset utilization expert 50 collects the data associated with the appropriate inputs and outputs for a model over a period of time from the process control network and stores this data for use by the models 56. Then, at the desired times, such as every minute, hour, day, etc., the asset utilization expert 50 may execute the regression analysis using the most recently collected sets of data to determine the best fit for the model parameters using the collected data. The sets of measured input and output data that are used in the regression analysis may be independent of or may overlap with the sets of data used in a previous regression analysis for that model. Thus, for example, a regression analysis for a particular model may be run every hour but may use input and output data collected every minute for the last two hours. As a result, half of the data used in any particular regression analysis may overlap with the data used in a previous regression analysis. This overlapping of data used in the regression analysis provides for more continuity or consistency in the calculation of the model parameters.

Similarly, a regression analysis can be performed to determine if sensors making measurements within the process 10 are drifting or have some other error associated therewith. Here, the same data or potentially different data pertaining to the measured inputs and outputs of the entity being modeled are collected and stored by, for example, the asset utilization expert 50. In this case, the model can be generally mathematically expressed as Y+dY=F(X+dX, P), wherein dY are the errors associated with the measurements of the outputs Y, and dX are the errors associated with measurements of the inputs X. Of course, these errors could be any types of errors, such as bias, drift, or non-linear errors and the model may recognize that the inputs X and outputs Y may have different kinds of errors associated therewith, with the different kinds of possible errors having different mathematical relationships to the actual measured values. In any event, a regression analysis, such as a maximum likelihood least squares or any other regression analysis may be performed using the model with the measured inputs and outputs to determine a best fit for the unknown sensor errors dY and dX. Here, the model parameters P may be based on the parameters P calculated using a previous regression analysis for the model or may be treated as further unknowns and may be determined in conjunction with this regression analysis. Of course, as the number of unknowns used within regression analysis increases, the amount of data required increases and the longer it takes to run the regression analysis. Furthermore, if desired, the regression analysis for determining the model parameters and the regression analysis for determining the sensor errors may be run independently and, if desired, at different periodic rates. This different periodicity may be beneficial when, for example, the time frame over which measurable sensor errors are likely to occur is much different, either greater than or less than, the time frame over which changes in the model parameters are likely to occur.

In any event, using these component models, the asset utilization expert 50 can perform asset performance monitoring by plotting the values of the determined model parameter(s) (and/or model inputs and outputs) versus time. Still further, the asset utilization expert 50 can detect potentially faulty sensors by comparing the determined sensor errors dY and dX to thresholds. If one or more of the sensors appears to have a high or an otherwise unacceptable error associated therewith, the asset utilization expert 50 can notify a maintenance person and/or a process control operator of the faulty sensor.

It will be understood from this discussion that the component models may be executed independently for different purposes at different times and, in many cases, may be executed periodically to perform the above described performance monitoring activities. Of course, the asset utilization expert 50 can control the execution of the appropriate models for the appropriate purposes and use the results of these models for asset performance monitoring and optimization. It will be understood that the same model may be run by the asset utilization expert 50 for different purposes and for calculating different parameters or variables associated with the model.

As noted above, the parameters, inputs, outputs or other variables associated with any particular model may be stored and tracked to provide performance monitoring for a device, a unit, a loop, an area or any other entity of a process or a plant. If desired, two or more of these variables may be tracked or monitored together to provide a multi-dimensional plot or measure of the performance of the entity. As part of this performance modeling, the location of the parameters or other variables within this multi-dimensional plot may be compared to thresholds to see if the entity, as defined by the coordinated parameters being monitored, is within a desired or acceptable region or is, instead, outside of that region. In this manner, the performance of an entity may be based on one or more parameters or other variables associated with that entity. FIG. 5 illustrates a two-dimensional plot of the operating region of an entity, such as the distillation column of FIG. 4, as defined by the values of the parameters P1 and P2 for this entity. Here, the parameters P1 and P2 (which may be determined using the model regression analysis described above or in any other desired manner) are plotted in a two-dimensional manner and the points on the plot (each being defined by a value for P1 and a value for P2) are determined for different times illustrated as T1-T10. Thus the point XT1 represents the point defined by the values for the parameters P1 and P2 at time T1. The points XT1 to XT10 on the plot of FIG. 5 illustrate that the entity was operating within a desired region (region 1) between the times T1 and T6, entered a less desirable but acceptable region (region 2) at time T7 and entered an unacceptable or failing region (region 3) at time T10. Of course, the boundaries of these different regions are determined beforehand by, for example, an expert and are stored in the computer 30 for access by the asset utilization expert 50 at any desired time. While FIG. 5 illustrates a two-dimensional parameter performance monitoring technique, this same technique could be applied in one dimension or in three or more dimensions to effect performance monitoring. Furthermore, the regions or other information about the location of the entity in the n-dimensional plot may be used by, for example, the index generation routine 51, to generate a performance index.

It will be understood that the asset utilization expert 50 can monitor one or more entities using the monitoring technique described above based on model parameters or other model variables and can report the operating states or performance measures of these entities to any other desired persons, functions or applications within the process control plant 10, such as to a process control expert system, a maintenance person, a business application, a user interface routine 58, etc. Of course, it will also be understood that the asset utilization expert 50 may perform performance or condition monitoring on any desired entity, based on one, two, three or any other desired number of parameters or variables for each entity. The identity and number of variables or parameters to be used in this performance monitoring will generally be determined by an expert familiar with the process and will be based on the type of entity being monitored.

If desired, the asset utilization expert 50 may also define a performance index or plot by comparing one or more of the parameters determined by the models as described above with the same parameter determined by the model run in accordance with the design parameters of the entity being modeled. In particular, the asset utilization expert 50 may execute a model using the design parameters of the entity within the plant 10 to which the model pertains to determine what the designed performance of the entity would be if it was operating according to the current state of the process and using the actual inputs to the entity as measured within the plant 10. This design performance can then be compared to the actual performance of the entity as determined by the component model for that entity or as determined by the measured inputs and outputs of the entity to generate a measure of the performance of the entity.

Thus, for example, the efficiency of an entity may be determined using the component model that estimates the parameters of the entity (one of which may be efficiency) based on the regression analysis described above. At the same time, a model of the entity may be run using the parameters that would result according to the design criteria of the entity, but based on the actual inputs to and/or outputs from the entity. Thus, for example, if a different raw material was being input to the entity, the design model would be run using the efficiency that would result from the change in raw materials. The performance of the entity in both cases may be compared to determine a performance index which indicates how far from the possible or designed operation the actual entity is operating. This performance index can then be reported to and used by other applications or users of the system, such as a process control, a maintenance or a business person or application.

The component models 56 may also be used to perform process optimization. In particular, the asset utilization expert 50 may use one or more of the optimization routines 55 that execute the individual component models to optimize the operation of the plant in terms of some optimization criteria provided by, for example, a process control operator or a business person via a business application. The optimizer 55 can be a real time optimizer that operates in real time to optimize the plant 10
based on the actual state of the plant 10 at that time. Alternatively or additionally, an optimizer 55 may determine changes to be made to the plant 10, such as bringing certain devices or units back on line, that will provide the greatest optimization of the plant 10. Of course, other types of optimization routines 55 may be executed instead of or in addition to those mentioned here.

In one embodiment, the RTO+ real time optimization routine, which is provided by MDC Inc., may be used as a real time optimizer and may be executed at various or periodic times during operation of the plant 10, such as every 3-5 minutes, every
10-15 minutes, every hour, etc. Of course, other known or later developed optimizer routines which perform optimization less frequently, such as every 3 or 4 hours or every 3 to 4 days could be used instead.

The RTO+ optimizer implements three general phases each time it is executed to perform real time optimization. The RTO+ optimization routine first executes an input phase during which the routine checks to determine whether the variables that were previously indicated at the design of the optimizer as being variables which could be manipulated by the optimizer to perform optimization, such as set points or other inputs of various devices, units, etc., can actually be manipulated at the current time. This information may be available to the optimizer from the asset utilization expert 50, which obtains this information from the process control system and stores this information within any desired database. Thus, during the input phase, the optimizer actually determines, based on the data provided to it from the asset utilization expert 50, whether each of the possible manipulated inputs is still available to be changed. In many instances, one or more of the potential manipulated inputs may not be available for change due to the fact that, for example, a device which provides that input is not operating or has been taken off-line or the device is being run in a mode other than the designed mode thereby preventing the controller from changing inputs to the device.

As part of the input phase, the real time optimizer may also determine if the variables that were supposed to change during the last run of the optimizer were actually changed to and reached the suggested or calculated values from the last run of the optimizer, i.e., the values to which they were supposed to be changed. If a variable that was supposed to change to a particular value did not reach that value, the optimizer recognizes that there is a problem that is preventing the change from occurring and effectively removes the option of changing this variable to that value during the next run of the optimizer. Detecting the failure of a variable to reach a value which it should have theoretically reached may also cause the optimizer to indicate to an operator that there may be a problem within the system that needs to be addressed.

Next, during the input phase, the optimizer performs a quick execution of each of the individual component models that make up the entire model using, for example, the actual inputs and outputs measured from the plant 10. The calculated outputs of each component model are then reviewed to see if there is any problem with any particular component model that will prevent the entire model from running accurately. Here, the optimizer may use the actual measured inputs for each entity (which have previously stored) to see if the individual component parts of the model work for these actual inputs to produce realistic outputs.

Assuming that each of the component models can be executed, the optimizer may look for discrepancies in the models that may effect the ability of the optimizer to optimize. For example, the optimizer may determine if a measurement made by a real device is the same as predicted by the component model using the actual inputs to the device. If the model (using the most recently calculated model parameters) predicts an output that deviates from the actual measurement of the output, such as if the model predicts a flow rate of eighteen and a flow rate meter reads twenty, then the optimizer may reset a constraint associated with the flow rate at two below the previously defined constraint. Thus, if the constraint associated with that flow rate was originally set at twenty-five, the optimizer may use a constraint of twenty-three because the optimizer recognizes that its model is in error by two with respect to this variable. Of course, the optimizer may look for other inconsistencies or deviations between models and the actual measurements of the plant to reset, update or tweak constraints or other variables within the optimization routine.

In the next phase, known generally as the optimization phase, the optimizer runs the individual models in a predetermined order using the outputs from one component model as inputs to one or more of the other component models making up the entire model. Using the entire model, the constraints provided by the user and the new constraints determined by the input phase, as well as the optimization criteria, the optimizer determines the changes to be made to the input or manipulated variables that hav