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Building Cogeneration Planning Scheduling Systems using IBM ILOG ODME, CPLEX and impress
This document discusses using IBM's ODME and iMPress software for building cogeneration planning and scheduling applications. It provides an overview of ODME, iMPress, and how they can be implemented together. Key benefits include fitting business models/processes, sophisticated optimization, quick adaptation, and scenario analysis. A proof-of-concept is proposed to select a plant type, integrate data sources, solve models, and tune models for accuracy and tractability.
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Building Cogeneration Planning Scheduling Systems using IBM ILOG ODME, CPLEX and impress
- 1. Building Cogeneration Planning andScheduling Applications using IBM ODME and iMPress DecisionBrain & Industrial Algorithms LLC. 7/19/2013 Copyright, DB & IAL
- 2. Agenda • What isODME? • What are Industrial Modeling Frameworks? • What is iMPress? • ODME-iMPress Implementation • Benefits • Proof of Concept 2
- 3. 3 Based on IBMILOG 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) Embedsall 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 standsfor “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 • Sometimescalled « 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 • Typicallyassumes 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 • Animportant 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
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- 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
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- 17. ODME-iMPress-CPLEX System Architecture • Adomain-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 • Javacode 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.
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- 23. Gantt Chart forReference (Base)
- 24. Trend Plots forReference (Base)
- 25. Demand Variability ScenarioData w/ Reference in ()
- 26. Trend Plots forDemand Variability Scenario w/ Reference
- 27. Benefits • Perfectly fityour 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) • Selectplant 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).