Abstract:
The disclosure relates to troubleshooting process issues using an Expert System. Remote Performance Management (RPM) data based on Distributed Control System (DCS) information is transmitted to an Expert System and subsequently translated. The Expert System determines a solution along with an associated confidence level. The Expert System may prioritize the solution an in so doing, signal the DCS to automatically implement the transmitted solution.

Description:
FIELD OF THE INVENTION 
       [0001]    The disclosure relates to integrating Expert Systems with data collection and monitoring systems to improve troubleshooting of process issues. 
       BACKGROUND 
       [0002]    Currently, Expert Systems (ES) and/or Expert System tools (ES tools) are used to troubleshoot process issues. In an exemplary system, smart questionnaires which ask users questions about the process variables may be utilized. The smart questionnaires can be advantageous since heuristics can be programmed into the decision tree logic. Process technology experts determine these heuristics. These rules of thumb may be modified slightly per customer process unit because these process technologies are adaptable to specific customer demands (i.e. refinery purpose such as maximizing gasoline or diesel, etc. . . . ). ES tools currently in use are used by field service advisors, but can also be used by customers when a process issue develops. However, current Expert Systems are qualitative in nature and are primarily designed for use by experts for consulting purposes. 
         [0003]    As shown in  FIG. 1 , Remote Performance Management (RPM) system  102  collects process variable data from a process unit  104  and records the collected data through use of a Distributed Control System (DCS)  106 . DCS system  106  controls process unit  104  through numerous instrumentation and control devices (not shown) found on process unit  104 . Process unit  104  operators can manually alter DCS  106  set points to change process variables. Such process variables include but are not limited to temperature and pressure. 
         [0004]    When a process problem, such as a process upset, arises on process unit  104 , information from DCS  106 , which directly controls process variables in an effort to maintain steady-state or pseudo-steady-state, may be used by RPM system  102  to output graphs and trends. The data may then be reviewed by a user  108  (using a workstation or computer) and changes made to DCS  106  set points to alter the process outputs. Depending on the user type (customer, process technology expert, or novice field advisor), an Expert System (ES) system  110  may be consulted offline to assist in determining appropriate process variables to alter. The RPM system  102  is quantitative in nature, thus needing graphical and analytical interpretation by a process technology expert. 
         [0005]    A need exists for integrating the RPM system  102  with an ES  110  to assist in the timely and accurate troubleshooting of process problems. The combined system may economically increase yields from existing sources and/or reduce utility energy usage while efficiently resolving process issues more quickly. 
       SUMMARY 
       [0006]    Aspects of the disclosure overcome problems and limitations of the prior art by providing a method of troubleshooting a process unit upset event or anticipated process unit upset event for a process unit. 
         [0007]    An aspect of the disclosure includes translation of RPM data which may be quantitative in nature into data for use by an Expert System. In an embodiment, coupling these two systems may decrease the time required for troubleshooting process issues that need to be resolved. In addition, the coupling of these two systems may also reduce the likelihood of a future process unit upset event or eliminate the occurrence. 
         [0008]    In an embodiment, information from a RPM flows to an ES and a user. The ES may determine a solution or course of action to overcome the current or anticipated process unit upset. A user may then incorporate both the RPM and ES outputs to finalize the resolution. This resolution would then be routed to the DCS for implementation of the process variable changes. 
         [0009]    In an additional embodiment, RPM data flows to a user and an ES. The ES determines a solution and directly makes changes to process variables via the DCS. The user may modify the heuristics of the ES for the particular unit, if desired. 
         [0010]    In another aspect of the disclosure, an ES may determine a confidence level in a determined solution based on translated variable data. In addition, the determined solution may be prioritized based on the determined confidence level. The determined solution may be transmitted to a DCS for automatic execution of the determined solution. 
         [0011]    The details of these and other embodiments of the present disclosure are set forth in the accompanying drawings and the description below. Other features and advantages of the disclosure will be apparent from the description and drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWING 
         [0012]    A more complete understanding of the present disclosure and the advantages thereof may be acquired by referring to the following description in consideration of the accompanying drawings, in which like reference numbers indicate like features. 
           [0013]      FIG. 1  shows a prior art process for troubleshooting process issues. 
           [0014]      FIG. 2  shows a diagram of a computer system that may be used to implement various aspects of the disclosure. 
           [0015]      FIG. 3  illustrates system components of the computer system shown in  FIG. 2  which may be used to implement various aspects of the disclosure. 
           [0016]      FIG. 4  illustrates a supplemental work process or method for troubleshooting process issues in accordance with various aspects of the disclosure. 
           [0017]      FIG. 5  illustrates an additional automated work process or method for troubleshooting process issues in accordance with various aspects of the disclosure. 
           [0018]      FIG. 6  illustrates a table showing translation of data for use in troubleshooting process issues in accordance with an embodiment of the disclosure. 
           [0019]      FIG. 7  illustrates a flow diagram in accordance with an embodiment of the disclosure. 
       
    
    
     DETAILED DESCRIPTION 
       [0020]    In the following description of the various embodiments reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration various embodiments in which the disclosure may be practiced. It is to be understood that other embodiments may be utilized and structural and functional modifications may be made without departing from the scope of the present disclosure. 
         [0021]      FIG. 2  shows a diagram of a computer system  200  that includes a DCS  106  that performs as the controlling entity for automation devices. DCS  106  may directly or indirectly control automation devices located within the scope of a process unit. In an embodiment, display devices  203  may give a visual representation of the layout of process unit  104  ( FIG. 1 ) with an overlay of the control and instrumentation devices utilized to operate process unit  104 . 
         [0022]    In an aspect of the disclosure, automation devices may be directly controlled through DCS  106  via a TCP/IP (or UDP/IP)  204  communication pathway. In an embodiment, DCS  106  may include I/O controllers  205  and  206  which may be situated between the automation devices and the DCS  106 . For instance, automation devices may include compressor  208 , pump  210 , and thermocouple  212 . In another embodiment, remote I/O controller  206  may be situated between DCS  106  and automation devices such as pilot light  214 , relay  216 , push button  218 , gate valve  220 , and/or mixing valve  222 . Those skilled in the art will realize that numerous additional automation devices may be connected to DCS  106 . 
         [0023]    In another aspect of the disclosure, DCS  106  may also be in communication with a programmable logic controller (PLC) or programmable controller  224 , which is typically a digital computer, and is often used for automation of industrial processes (e.g., control of machinery on factory assembly lines, control of chemical processes, control of amusement rides, and control of lighting fixtures). Unlike general-purpose computers, a PLC  224  is designed for multiple inputs and output arrangements, extended temperature ranges, immunity to electrical noise, and resistance to vibration and impact. Programs to control machine operation are typically stored in battery-backed or non-volatile memory. The DCS  106  may control various inputs and outputs via PLC  224 . 
         [0024]    In an aspect of the disclosure, a plurality of user computers or workstations such as  108  and  226  may be coupled to DCS  106  via a network  232 . User computers or workstations  108  and  226  may provide decision makers with a user interface for displaying information to assist in troubleshooting process problems. 
         [0025]    The term “network” as used herein and depicted in the drawings should be broadly interpreted to include not only systems in which devices are coupled together via one or more communication paths, but also stand-alone devices that may be coupled, from time to time, to such systems. Consequently, the term “network” includes not only a “physical network” but also a “content network,” which is comprised of the data—attributable to a single entity—which resides across all physical networks. 
         [0026]    In an aspect of the disclosure, RPM  102  may also be in communication with DCS  106  and user computers  108  and  226  through network  232 . RPM  102  may directly or indirectly collect process variable data from DCS  106  and record them for resolving process upsets. In another embodiment, an integrated or non-integrated data repository  234  may also be utilized to store collected process data. 
         [0027]    In an aspect of the disclosure, Expert System  110  may be used to assist in troubleshooting process issues. For instance, Expert System  110  may be in communication with RPM  102 , DCS  106 , and various user workstations  108  and  226  through network  232  to communicate data associated with process upsets. 
         [0028]      FIG. 3  illustrates system components of the computer system shown in  FIG. 2  which may be used to implement various aspects of the disclosure. In  FIG. 3 , RPM  102  is shown in communication with DCS  106 , user computer  108 , and ES  110  via network  232 . One skilled in the art will appreciate that network  232  may be replaced with additional or different computer networks. For example, one or more networks may be in the form of a local area network (LAN) that has one or more of the well-known LAN topologies and may use a variety of different protocols, such as Ethernet. One or more of the networks may be in the form of a wide area network (WAN), such as the Internet. Computer devices and other devices may be connected to one or more of the networks via twisted pair wires, coaxial cable, fiber optics, radio waves or other media. 
         [0029]    In an aspect of the disclosure, RPM  102  may be programmed with computer-executable instructions to receive data from DCS  106  and or user computer  108 , format the requests and transmit the requests to Expert System  110 . In one embodiment of the disclosure, requests for data are in the form of documents that are in extensible markup language (XML) format. 
         [0030]    In  FIG. 3 , RPM  102  may include memory  302 , processor  304 , communication interface  306  and display  308 . User computer  108  and ES  110  may also contain comparable components to process data received from DCS  106  and RPM  102 . In addition, ES  110  may also include a translation module  320  for processing and translating and/or transforming data from RPM  102 . 
         [0031]      FIG. 4  illustrates a work process or method for troubleshooting process issues in accordance with various aspects of the disclosure. In  FIG. 4 , information from RPM  102  may be transmitted to ES  110  and user computer  108 . The data received by ES  110  may be used to troubleshoot a current or unforeseen process issue in progress on process unit  104 . In an embodiment, ES  110  may be running real time and not as an offline process module. 
         [0032]    In an aspect of the disclosure, ES  110  may recommend a solution to the process issue and transmit that recommendation to the user computer  108  for integration with graphical, analytical, statistical, translated, transformed or other data received from RPM  102 . A final resolution to the process issue may then be transmitted to DCS  106  for implementation. 
         [0033]      FIG. 5  illustrates another work process or method for troubleshooting process issues in accordance with various aspects of the disclosure. In  FIG. 5 , information from RPM  102  may be transmitted to ES  110  and user computer  108 . The data received by ES  110  may be used to troubleshoot a current or unforeseen process issue in progress on process unit  104 . In an embodiment, ES  110  may be running real time and not as an offline module or component. 
         [0034]    In an aspect of the disclosure, ES  110  may determine a solution to the process problem and transmit the solution to DCS  106  for direct and automatic implementation. User computer  108  may monitor the solution from ES  110  as well as information received from RPM  102 . 
         [0035]    In an aspect of the disclosure, a transition time period from the embodiment described in  FIG. 4  to the embodiment described in  FIG. 5 , may be utilized in order to verify ES  110  output. In an embodiment, the translated or transformed data from RPM  102  may have to be adjusted and/or translated so that ES  110  may determine a solution consistent with a solution independently provided by user computer  108 . For instance, graphical and/or analytic data from RPM  102  may be quantitative in nature, whereas, the data used by the Expert System data may be more qualitative in nature. 
         [0036]    In an embodiment of the disclosure, translation and/or transformation of data from RPM  102  may be executed by translation module  320  ( FIG. 3 ) in ES  110 . Translation module  320  may execute an algorithm temporarily or permanently stored in memory  302  of ES  110 . 
         [0037]    The following exemplary embodiment is used for explanatory purposes and is not intended to be limiting to the scope of the disclosure. For instance, the temperature of a C5/C6 isomerization process unit reactor bed may need to be analyzed. Those skilled in the art will realize that additional process variables other than temperature may also be analyzed and controlled by the methods of this disclosure. 
         [0038]      FIG. 6  illustrates a table showing the day and time  602  that a reactor bed temperature RxBedTemp 1   604  was recorded in accordance with an aspect of the disclosure. For instance, RxBedTemp 1   604  shows temperatures for dates ranging from Jul. 14, 2008 to Aug. 15, 2008 for a particular C5/C6 isomerization process unit. Those skilled in the art will realize that the portion of data shown is merely illustrative. Those skilled in the art will realize the absence of unit measurements here within is irrelevant to the scope and intent of this embodiment. 
         [0039]      FIG. 6  also shows the change of temperature of the reactor bed from the previous day measurement at  606 . In an aspect of the disclosure, statistical confidence interval values of the 25 th  percentile  608 , 50 th  percentile  610 , and 75 th  percentile  612  may be calculated. In an embodiment, these percentiles may be calculated using a statistical confidence interval calculation basis period of the previous 30 days (which includes the current day). Those skilled in the art will realize that the choice of statistical confidence interval values and statistical confidence interval calculation basis period shown above are merely illustrative. 
         [0040]      FIG. 6  illustrates in tabular form calculations to determine if the reactor bed temperature lies within the middle of the 50% of data (does the temperature fall between the first and third quartile) at  614 . In addition,  FIG. 6  also illustrates calculations to determine average bed temperature using an average calculation basis period of the previous  30  days if the data is within the middle 50% of data at  616 . Those skilled in the art will realize that the choice of average calculation basis period used above is merely illustrative. 
         [0041]    Next, a standard deviation  620  may be calculated (see intermediate step  618 ) using the average calculation period of the previous 30 days (including the current day) if these 30 data points lie within the middle two quartiles of the distributed data in accordance with an aspect of the disclosure. Those skilled in the art will realize that the choice of average calculation basis period used above is merely illustrative. 
         [0042]    Then in an embodiment, a comparison of the standard deviation  620  to the change in temperature  606  between the current day and the previous day may be determined by calculating the ratio  622 . If the calculated absolute value of the ratio  622  is greater than the high level cut-off point of 10, then ES  110  may assign a high level of confidence  624  in solutions it determines for the process variable deviation. In an aspect of the disclosure, ES  110  may also highly prioritize troubleshooting of this variable. Those skilled in the art will realize the choice of all cut-off points here within are merely illustrative. 
         [0043]    In another aspect of the disclosure, if the calculated absolute value of the ratio  622  is greater than the medium level cut-off point of 3, but less than the high level cutoff point of 10, then ES  110  may assign a medium level of confidence  624  in solutions it determines for the process variable deviation. In an aspect of the disclosure, ES  110  may also prioritize troubleshooting of this variable to an extent less than the variable that was determined above. 
         [0044]    In another aspect of the disclosure, if the calculated absolute value of the ratio  622  is less than the low level cut-off point of 3, then ES  110  may assign a low level of confidence  624  in solutions it determines for the process variable deviation. In an aspect of the disclosure, ES  110  may also deemphasize prioritization for troubleshooting of this variable as compared to the variables that were determined above. 
         [0045]    For further clarity, the following listing describes the specific algorithm illustrated in  FIG. 6  to determine the values listed in  FIG. 6 . 
         [0046]    Tag  602 =Time=the day and time the data was recorded in RPM 
         [0047]    Tag  604 =RxBedTemp 1 =the temperature of reactor bed # 1   
         [0048]    Tag  606 =Delta RxBedTemp 1 =the change in temperature of this bed from the previous day 
         [0049]    Tag  608 =25th Percentile=the value of the first quartile based only on the previous 30 days of data 
         [0050]    Tag  610 =50th Percentile=the value of the second quartile based only on the previous 30 days of data 
         [0051]    Tag  612 =75th Percentile=the value of the third quartile based only on the previous 30 days of data 
         [0052]    Tag  614 =Does RxBedTemp 1  Lie within the Middle 50% of Data=Is the numerical value of RxBedTemp 1  between the first and third quartiles? 1=yes, 2=no 
         [0053]    Tag  616 =Average RxBedTemp=Computes the average RxBedTemp 1  over the past 30 days if the data is within the middle 50% of data 
         [0054]    Tag  618 =Intermediate Calculation=This is an intermediate calculation to determine the standard deviation 
         [0055]    Tag  620 =Standard Deviation of the Middle 50% of Data=Calculates the standard deviation for the middle 50% of data over the past 30 days 
         [0056]    Tag  622 =Ratio=Delta RxBedTemp 1  divided by the standard deviation 
         [0057]    Tag  624 =Alarm=HIGH: |Ratio|&gt;10, MEDIUM: 3&lt;|Ratio|&lt;10, and LOW: |Ratio|&lt;3 
         [0058]    In an aspect of the disclosure, a method of troubleshooting a process unit  104  upset event or an anticipated process unit  104  upset event for a process unit may be implemented. Referring to  FIG. 7 , an Expert System  110  may receive process unit variable data for the event based upon a determination of a process unit event upset as shown in step  702 . Next, in step  704 , the process unit variable data may be translated and/or transformed. In step  706 , a solution may be determined based on the received process unit variable data. A confidence level may be determined for the determined solution in step  708 . In an embodiment, the confidence level may be determined in tandem with step  706 . The determined solution may be prioritized in step  710  based on the confidence level. In step  712 , the determined solution may be transmitted to a distributed control system  106  for automatic execution as shown in step  714 . Those skilled in the art will realize that these prioritized solutions are derived from analysis of alarm  624  and from various combinations of process variables. In another aspect of the disclosure, prioritization of determined solutions may also be based on previous day results since ΔT (for example) may be corrected one day but still generate an alarm. 
         [0059]    In an aspect of the disclosure, the recited parameters may be adjustable by a process technical expert or user. For instance, some of the parameters that may be adjusted include:
       1) n in n*σ for High, Medium, and Low criteria,   2) Number of days σ is based on,   3) Breakup of data “confidence intervals”       
 
         [0063]    Furthermore, those skilled in the art will realize that the specific numbers involved in the above exemplary embodiment may be modified as per a process technology expert&#39;s experience for different process unit types, process unit locations, and/or customer requirements. For example the standard deviation may use additional data points or fewer data points or non-consecutive and segmented data points of data quality issues exist, and may also exclude the two 25% ends on data, and the ratio cut-offs for the alarms. 
         [0064]    While the disclosure has been described with respect to specific examples including presently preferred modes of carrying out the invention, those skilled in the art will appreciate that there are numerous variations and permutations of the above described systems and techniques that fall within the spirit and scope of the invention.