2. SSE-11 Solving Industrial Problems with Non-traditional Applications of ROMeo
Larry S. Balcom
Senior Principal Technical Sales Consultant
(c)
(e)
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3. What makes ROMeo a unique application development platform?
Simple, yet powerful, graphical real-time solution automation using RTS
Excellent solution integration into the industrial IT architecture
Flexible, extensible model data structures
Robust solver and open equation platform for custom properties
Easy, open source programming language to extend functional capability

4. What kinds of Industrial Problems can ROMeo solve?
Transaction Processing
Compositional Tracking
Operational Validation
Soft Sensing and Data Preparation
Environmental Analysis
Process Analytics
Real-time Statistical Process Control

5. Transaction Processing Compositional Tracking
Rigorously calculate unmeasured compositions and flows
Reconcile data to improve measurement quality, and detect errors
Close material balance, and calculate losses
Track vapor return for net delivered calculation
Accumulate fuel for terminal operation
Store shipments from unloading reports
Accumulate client’s deemed N2 for Send out
Virtual inventory for each client

6. Transaction Processing Compositional Tracking
Unloading
Transaction
Data
Historian
Integrate business systems
Calculate customer positions
Total inventories
Client Inventories

7. Transaction Processing Compositional Tracking
Nomination
Transaction
Data
Calculate operating costs
Fuel Gas
Deemed Nitrogen
Lost and Unaccounted For
FIFO shipments
Historian

8. Transaction Processing Compositional Tracking
Transaction files can be deleted or archived after processing

9. Transaction Processing Compositional Tracking
Transactions
Solve
Output

10. Transaction Processing Manual Operations Validator
Control
System
& SCADA
Operational
Alarms
Work Order Interface
system
Data
Historian
Work
Oder
Data
Visual representation
of future state
CSV
Raw
Process
Data
CSV
Model
Data
ROMeo Server
EDI data interfaces
Data Validation
Simplified Hydraulic Model

11. Soft Sensing and Data Preparation Environmental Analysis

12. Process Analytics Real-time Statistical Process Control
Process data, PAT Analyzers, and Batch control information
Wonderware
InTouch® & QI-Analyst®
SimSci-Esscor
Connoisseur®
Wonderware
Historian & System Platform®
QbD Visualization
SPC charts, Run charts, Process analysis
APC
(Advanced Process Control)
Dynamic Optimization
Data
Historian
Online-SPC
(Statistical Process Control)
Soft-Sensors
Process Analysis
Golden Batches
Aim, Sigma, Batch synchronization
SimSci-Esscor
ROMeo/ARPM®

13. Process Analytics Real-time Statistical Process Control
Data Historians
Control Systems
Intelligent Instruments
(OPC, COM, OLE, ODBC)
Wonderware InTouch®
Expert Advice
System
Application
Batch SPC Data
and Alarms
SPC Alarms,
Aim & Sigma
Process
Data
SPC Chart
Application
Batch Run
Charts
Visualization
Client
Online-SPC Server
ROMeo® Platform
Data Reconciliation is available
Golden Batches
Aim’s
Sigma’s
Batch Synchronization

14. Process Analytics Real-time Statistical Process Control
The typical WECO “out of control rules” are pre-defined: Rule 1: One point outside the 3 Sigma limits Rule 2: Two out of three points outside the 2 Sigma limit Rule 3: Four out of five points outside the 1 Sigma limit Rule 4: Eight consecutive points above or below the Aim Rule 5: Six points in a trend, increasing or decreasing Rule 6: Fourteen points alternating across the Aim

15. Process Analytics Real-time Statistical Process Control
Rules 2 through 4 and 6 require a zone tracking variable that stores the process pattern for future analysis. The digital zone encoding is: Zone 8: > +3σ Zone 7: = or < +3σ Zone 6: = or < +2σ Zone 5: = or < +1σ Zone 4: = or > -1σ Zone 3: = or > -2σ Zone 2: = or > -3σ Zone 1: < -3σ

16. Process Analytics Real-time Statistical Process Control
The tracking variable will be the length required to evaluate the longest rule. Rule 6 requires a 14 digit number, and is generally the longest rule defined. Recognize that this tracking methodology is limited by the numerical precise of ROMeo variables. Each model execution deletes the left digit, appends the current zone to the right, and analyzes the rules. If the sum is > the rule limit, then it’s an alarm state. The first alarm state found stops processing, and sets the alarm and latch flags. This means that the rules must be evaluated in highest to lowest priority order.

17. Process Analytics Real-time Statistical Process Control
Rule 5 requires adaptation from the classical SPC definition. In batch applications, the aim is not constant and trending up or down may be the normal process behavior. Implementing this rule as described will create false alarms. The intent of the rule is to detect when the process is trending in one direction or the other from the process aim. This can be implemented in the batch context by comparing the relative positions of the process values compared to the Aim and control limits. Relative position (RPV) is calculated as: RPV = (( PV – Aim ) / ( 3 * σ ) ) Where: PV is the current process value Aim is the current Aim value from the GBP σ is the current standard deviation from the GBP The RPV’s are now normalized versus the changing Aim and Sigma, and can be compared for trending. Additional storage variables are used for the trend pattern, and storing the last RPV.

18. Non-traditional Applications of ROMeo Summary
There are many unique and valuable ways to apply the ROMeo platform to solve industrial problems. We have see just a few here this morning.
Anytime you need real-time information, to integrate process data or transactional information, and need to digest the data into INFORMATION…. It’s a potential ROMeo application.
The solutions that we see are limited only by your creativity!

19. Q & A Larry S. Balcom Senior Principal Technical Sales Consultant
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