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Building Cogeneration Planning Scheduling Systems using IBM ILOG ODME, CPLEX and impress
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Building Cogeneration Planning Scheduling Systems using IBM ILOG ODME, CPLEX and impress


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  • 1. Building Cogeneration Planning and Scheduling Applications using IBM ODME and iMPress DecisionBrain & Industrial Algorithms LLC. 7/19/2013 Copyright, DB & IAL
  • 2. Agenda • What is ODME? • What are Industrial Modeling Frameworks? • What is iMPress? • ODME-iMPress Implementation • Benefits • Proof of Concept 2
  • 3. 3 Based on IBM ILOG Optimization Portfolio Engines and Tools CPLEX Optimization High-performance mathematical and constraint programming solvers, modeling language, and development environment Solution Platform ODM Enterprise Build and deploy analytical decision support applications based on optimization technology Oil&Gas Production Scheduling
  • 4. ILOG ODM Enterprise Architecture (OR) (IT) Embeds all CPLEX Optimization Studio Reporting Data Integration Data Modeling ODM Enterprise IDE ODM Enterprise Optimization Server/Engine ODM Enterprise Client & Planner Optimization Modeling, Tuning, Debugging Application UI Configuration (LoB) Development Deployment Application UI Customization Business Use Custom GUI Batch process ODM Enterprise Data Server
  • 5. Industrial Modeling Frameworks (iMF’s) • Process industry business problems are complex hence an iMF provides a pre-project or pre-solution advantage (head-start). • An iMF embeds intellectual-property and know-how related to the process’s flowsheet modeling as well as its problem-solving methodology.
  • 6. iMPress • iMPress stands for “Industrial Modeling & PRE-Solving System” and is our proprietary platform for discrete and nonlinear modeling. • iMPress can “interface”, “interact”, “model” and “solve” any production-chain, supply- chain, demand-chain and/or value-chain optimization problem. 6
  • 7. Cogenerartion Scheduling Application Types • Off-Line Environments: – Usually « dynamic » optimization with discrete (logic) & linear variables using Mixed Integer Linear Programming not including feedback (feedforward only). • On-Line Environments: – Usually « steady-state » optimization with continuous & nonlinear variables using NLP including feedback (and feedforward). – Usually includes steady-state detection, data reconciliation and regression (« moving horizon estimation ») with diagnostics for monitoring.
  • 8. Off-Line Optimization • Sometimes called « load shedding, shifting & scheduling ». – Determines steam and power production subject to supply availability and demand requirements. – Respects transition (sequence-dependent) management of producing units such as boilers and turbogenerators i.e., understands resting (standby), ramping (startup/shutdown) and running (setup) which IAL calls « Phasing ». – Similar to a « product wheel » found in specialty batch & fast moving consumer goods industries.
  • 9. Off-Line Optimization – « Phasing » • « Phasing » forces a predictable operational sequence or order for selected units.
  • 10. Off-Line Optimization – « Phasing » • REST = min. 3-d, RAMPUP = 1-d, RUN’s = min. 3 - max. 10-d, RAMPDOWN = 1-d, Past- Horizon = 2-d & Future-Horizon = 60-d.
  • 11. On-Line Optimization • Typically assumes discrete/logic variables are fixed – IAL calls this « phenomenological decomposition ». • If plant is at « steady-state* » then optimize process or operating conditions using NLP (IPOPT, KNITRO, XPRESS-SLP, IAL- SLPQPE). • Apply nonlinear data reconciliation & parameter estimation to provide gross- error/outlier detection & calibrate model. * Kelly & Hedengren, « A steady-state detection algorithm to detect non-stationary drifts in processes », Journal of Process Control, 23, 2013.
  • 12. On-Line Optimization • An important aspect is to callout/callback to physical/thermodynamic properties such as enthalpies. – STEAM67.DLL is « wrapped » in STEAM67_H.DLL to compute saturated enthalpy and its first-order derivatives using its saturated temperature. &sCondition HOTF COLDF WARMF HOTT COLDT WARMT FBAL HFBAL &sCondition &sCoefficient,@sType,@sPath_Name,@sLibrary_Name,@sFunction_Name,@iNumber_Conditions,@rPerturb_Size,@sConditi on_Names HOTH,dynamic,c:IndustrialAlgorithmsPhysicalPropertiesDebug,steam67_H,steam67_H,1,1e-6,HOTT COLDH,dynamic,c:IndustrialAlgorithmsPhysicalPropertiesDebug,steam67_H,steam67_H,1,1e-6,COLDT WARMH,dynamic,c:IndustrialAlgorithmsPhysicalPropertiesDebug,steam67_H,steam67_H,1,1e-6,WARMT &sCoefficient,@sType,@sPath_Name,@sLibrary_Name,@sFunction_Name,@iNumber_Conditions,@rPerturb_Size,@sConditi on_Names Conditions-&sMacro,@sValue FBAL,HOTF + COLDF - WARMF HFBAL,HOTH*HOTF + COLDH*COLDF - WARMH*WARMF Conditions-&sMacro,@sValue
  • 13. Cogeneration (Steam/Power) iMf 7/19/2013 Copyright, Industrial Algorithms LLC • Time Horizon: 168 time-periods w/ hour periods. • Continuous Variables = 5,000 • Binary Variables = 1,000 • Constraints = 7,500 • Time to First Good Solution = 5 to 30- seconds • Time to Provably Optimal = 5 to 15-minutes
  • 14. Cogeneration (Steam/Power) iMf Water Pump
  • 15. • Time Horizon: 168 time-periods w/ hour periods. • Continuous Variables = 5,000 • Binary Variables = 1,000 • Constraints = 7,500 • Time to First Good Solution = 5 to 30-seconds • Time to Provably Optimal = 5 to 15-minutes. • Solver: CPLEX 7/19/2013Copyright, Industrial Algorithms LLC Cogeneration (Steam/Power) iMf
  • 16. ODME-iMPress-CPLEX System Architecture
  • 17. ODME-iMPress-CPLEX System Architecture • A domain-specific data model was created in ODME using the usual master-data and transactional-data partitions. • A mapping between iMPress’ data model and ODME’s data model was established. • Java code was written to export iMPress’ IML file (Industrial Modeling Language). • SWIG Java was used to create a Java Native Inerface (JNI) to iMPress.
  • 18. ODME-IMPRESS-CPLEX System Architecture • Java code was written to call iMPress-CPLEX using its API’s. • Java code was written to access the solution(s) from iMPress-CPLEX using its API’s and to populate the ODME solution-data partition.
  • 19. ODME Screen Shots
  • 20. Data-Model in ODME
  • 21. Master-Data
  • 22. Transactional-Data
  • 23. Gantt Chart for Reference (Base)
  • 24. Trend Plots for Reference (Base)
  • 25. Demand Variability Scenario Data w/ Reference in ()
  • 26. Trend Plots for Demand Variability Scenario w/ Reference
  • 27. Benefits • Perfectly fit your business model and decision processes • Sophisticated optimization capabilities able to tackle complex, non-linear and large-scale problems • A solution that can be quickly adapted to new production processes • A user-friendly GUI to help planners driving refinery operational excellence and analyzing refinery behavior • What-if scenario analysis for confident decision-making • See all your data and options in one place with drill-downs and graphics • Collaborate with other planners • Powered by IBM ILOG CPLEX Optimizers
  • 28. Proof-of-Concept (POC) • Select plant type, size and complexity. • Determine if off-line or on-line application. • Configure plant model. • Integrate data sources. • Solve plant model with plant data. • Tune plant model (for accuracy & tractability).