Leveraging process models across the asset lifecycle t fiske arc


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Leveraging process models across the asset lifecycle t fiske arc

  1. 1. Leveraging P L i ProcessModels Across the Asset Lifecycle Tom Fiske Senior Analyst ARC Advisory Group tfiske@arcweb.com
  2. 2. Process Modeling and SimulationModeling LifecycleDesign PhaseStartupOperationsSummary and Recommendations 2 © ARC Advisory Group
  3. 3. Process Modeling and Simulation Models • A model is a simplified representation of a system at some point in time or space intended to promote understanding of the real system Simulation • Process simulation uses computer-based modeling of a system to understand its g y behavior and predict the effect of changes • Process modelers are primarily interested in representing the behavior of a real world real-world physical process by means of a reproducible, mathematical form • Simulation is a proven cost-effective way of exploring new processes and designs, without having to resort to expensive pilot programs or designs prototypes • The level of understanding, which may be developed through simulation, is seldom achievable by any other means Process Modeling and Simulation Provide a Safe and Inexpensive Method of Optimizing Design and Operations 3 © ARC Advisory Group
  4. 4. Types of Models Model Types Types of Simulation • First principles First-principles • Steady State • Empirical • Process Design Modeling Environments • Process Improvements • Flowsheets • E l t monitor & Evaluate, it • Open Equation troubleshoot plant performance • Dynamic • Controllability studies • Start-up procedures • Operator Training Simulators • Optimization Process & equipment 4 © ARC Advisory Group
  5. 5. Fidelity of Models Different Purposes Require Different Fidelity High Chemical Behavior Physical Fidelity Behavior Process Devices Process Signals Low FunctionalityAll Models Must be Based on Consistent Data and Information 5 © ARC Advisory Group
  6. 6. Modeling Environment Architecture Graphical Analysis Tool Numerical Solver Unit Operations Components Physical Properties Database Modeling Environments Are Well Suited for Engineers 6 © ARC Advisory Group
  7. 7. Model Deployment Training & Control System C lS Validation 23% Design Simulation 37% On-line Optimization 18% Off-line Optimization 22% Typical Applications Where Models Are Being Used 7 © ARC Advisory Group
  8. 8. Models Are Valuable Corporate AssetsModels Codify Knowledge and Generate Valuable Information Models M d l are a key element to the creation, capture, k l t t th ti t codification, and reuse of knowledge Models are built using all available knowledge of the process including: • R&D, pilot plant, and any operating data • Scientific principles • Human operators knowledge operators’ Models generate • Equipment specifications • P Process configuration fi ti • Processing parameters and product quality info Models Contain and Generate Process and Asset Information that Must Be Managed throughout the Lifecycle of an Asset 8 © ARC Advisory Group
  9. 9. Models Used throughout Plant Lifecycle Design Startup • Automation Validation and Checkout • Operator Training Operations • Process Analysis and Improvements • Process Monitoring • Equipment Monitoring • Operations Decision Support • Real-time O l Optimization • Operator Training Different Usage Don’t Always Use Consistent Information and Are Often Developed Independently 9 © ARC Advisory Group
  10. 10. Asset Creation Begins with Conceptual Design Design Design Models • Steady State Simulation often not be used afte sed after • Equipment Sizing and Specification design phase • Process and Plant Configuration • Economic Analysis • PFD • P&ID • Dynamic Simulation • Controllability Studies Process Design Integrated Front-End Engineering Design Models are • Process Simulation often created • Cost Evaluation by different • Design Tools organizations EPC s are good at internal collaboration and integration EPC’sEPC’s are less effective at the interfaces of other disciplines 10 © ARC Advisory Group
  11. 11. Startup Startup p • Automation Validation and Checkout • T i ll simplified d Typically i lifi d dynamic i models • Operator Training • Requires realistic modeling and use of realistic automation system s stem • Justification based on faster startup Modeling Effort for Startup Is Often Not Leveraged in Operations 11 © ARC Advisory Group
  12. 12. Models Used Extensively in Operations Operations • Steady State Simulation • Process improvements • Evaluate, monitor & troubleshoot plant performance • What if anal sis and p edicti e capabilit analysis predictive capability • Simplified front-ends • KPIs • Offline optimization • Operator guidance • Dynamic Simulation • Evaluate control strategies • Evaluate startups and shutdowns procedures Models Provide Basis for Digital Process Plant d l id i f i i l l 12 © ARC Advisory Group
  13. 13. Real-time Optimization (RTO) Resurgence of optimization solutions • Start small and expand • Target an economically important nonlinear aspect of a plant that provides sufficient economic benefit • Offline to online Economic Updating Objective • Multiple purpose – decision Criteria Process Model Function Optimization support, Asset Mgt. Steady Model Setpoint & Validate Data • Requires highly skilled expertise q g y p State Detection Data Reconciliation Parameter Tuning Steady State Check that is different from MPC Historian Real-time Database MPC • More difficult to implement Targets and maintain than MPC Distributed Control System • Use of external suppliers and independent contractors common • Expand use to include asset and performance management • Dynamic optimization RTO Models Require Significant Maintenance and Upkeep 13 © ARC Advisory Group
  14. 14. Operator Training Simulators (OTS) Used for process automation testing • Low fidelity • DCS FAT and SAT d • Benefits include faster time to startups Used for Operator Training • Both normal and abnormal situations • Benefits include • Increased operator proficiency • Less unscheduled downtime • Improved operational performance • Fewer abnormal situations that lead to equipment damage or worse Used for engineering and controllability studies Begin building models in the design phase – incremental approach Immersion technology OTS Models Require Significant Maintenance and Upkeep 14 © ARC Advisory Group
  15. 15. Data and Information Value Principle High The value of information is inversely proportional to the time it takes to become actionable Low Value Time High V The value of information is directly proportional to the number of people and systems collaborating Low Plant Enterprise Ecosystem Internal External Collaboration Breadth 15 © ARC Advisory Group
  16. 16. Knowledge Provides Greater Understanding Understanding EXT INDEPENDENCE Understanding Principles Knowledge Understanding Patterns CONTE Information Understanding Relationships Data UNDERSTANDING Knowledge Is a Key Enabler of the Knowledge Worker and Supports Problem Solving and Troubleshooting 16 © ARC Advisory Group
  17. 17. Solutions within Knowledge Context Increasing I Inbound Supply Chain Synchronization Increasing Value of Knowledge ormance and Value Planning and Scheduling Operational Advanced Process Decision Support Control Modeling and Process Monitoring Simulation UNDERSTANDING rational Perfo Equipment E i t Monitoring Outbound Analysis KNOWLEDGE Supply Chain Synchronization Process Contextualized INFORMATION Optimization Oper Data Predictive Analytics DATA Fault Detection Data Collection Reporting Data Data Visualization Aggregation Data High Capability Range and Collaboration Breadth 17 © ARC Advisory Group
  18. 18. Common Actionable Context Work Processes Data Model & State Relevant Right People Information Context Common Time Data Quality Mgmt Dynamic Plant Application Portfolio Reference Model Right time Right Place Co Common Actionable Context Leverages the o ct o ab e Co te t e e ages t e “Single Version of the Truth” 18 © ARC Advisory Group
  19. 19. What Is Possible: Asset Lifecycle Modeling Data reconciliation model Controller d i C t ll design Process design / dynamic model RTO model Dynamic Consistent Planning / optimization scheduling model Model/ model Database MPC prediction Training model simulator model Engineering analysis dynamic model 19 © ARC Advisory Group
  20. 20. What Is Possible: Asset Lifecycle Modeling Process Optimization • Create models during conceptual and basic design • Models turned over to operations • Models reflect original design, but equipment is purchased with over designed parameters • Models typically don’t reflect as-built • Process is often modified and models need to be updated • Models used to identify bottlenecks and process conditions y p and improvements, troubleshooting, what-if scenarios for new op conditions and materials • Tie in optimizers with p p planning and scheduling ( g g (format for data exchange is important) • Information requirements: • Operating limits equipment limits material properties limits, limits, properties, operating states, etc. 20 © ARC Advisory Group
  21. 21. What Is Possible Consistent Steady State and Dynamic Models • Plants run at various states so you need optimal path to optimal steady state • Dynamic simulation vs. steady state simulation • EPC create steady state models • Automation companies create dynamic models – simple to complex for p y p p DCS checkout and training • Need to leverage work at each stage Process Performance Monitoring and Asset Reliability • Rigorous modeling has capability to monitor process for performance and equipment for reliability Online optimization Decision support for operators and managers (excel) • Models used for asset reliability, performance, throughput, quality, material and energy savings, determine product mix, control strategies, optimization, equipment damage Use of standards such as cape open Remote simulation 21 © ARC Advisory Group
  22. 22. Summary and Recommendations Models are Corporate Assets Models are Used Throughout Asset Lifecycle • Often developed by different organizations for different purposes and are not based on consistent data Models Need to be Leveraged Throughout Lifecycle Models Need to be Managed Throughout Lifecycle 22 © ARC Advisory Group
  23. 23. Thank You for Your Attention For more information, contact the author at tfiske@arcweb.com or visit our web pages at www.arcweb.com 23 © ARC Advisory Group