1. Leveraging P
L i Process
Models Across the Asset
Lifecycle
Tom Fiske
Senior Analyst
ARC Advisory Group
tfiske@arcweb.com

2. Process Modeling and Simulation
Modeling Lifecycle
Design Phase
Startup
Operations
Summary and Recommendations
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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
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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
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5. Fidelity of Models
Different Purposes Require Different Fidelity
High
Chemical
Behavior
Physical
Fidelity Behavior
Process
Devices
Process
Signals
Low
Functionality
All Models Must be Based on Consistent Data and Information
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6. Modeling Environment Architecture
Graphical Analysis Tool
Numerical Solver
Unit Operations Components
Physical Properties Database
Modeling Environments Are Well Suited for Engineers
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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
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8. Models Are Valuable Corporate Assets
Models 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
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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
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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’s
EPC’s are less effective at the interfaces of other disciplines
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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
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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
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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
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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
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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
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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
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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
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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”
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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
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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.
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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
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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
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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
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