real time optimization of air separation plants
DESCRIPTION
In this presentation, the important aspects of an RTO application on air separation will be discussed includingthe general IT structure, functions of its different software components, important steps in completingsuch a project, challenges in optimization and corresponding solutions.TRANSCRIPT
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Real Time Optimization of Air Separation Plants
Tong Li, Thierry Roba, Marc Bastid, and Amogh Prabhu
2
Presenter
• Amogh Vishwanath Prabhu entered graduate school at the University of Texas at Austin in August, 2004 and worked as a graduate research assistant
• During his study at the University of Texas at Austin, he also served as a Teaching Assistant in the fall of 2005 and a graduate level co-op at Advanced Micro Devices, Inc. during the spring and the summer of 2006, and the spring of 2007
• He received his PhD on Performance Monitoring of Run-to-Run Control Systems Used in Semiconductor Manufacturing in the summer of 2008
• Mr. Prabhu is currently employed at Air Liquide R&D, North America since the fall of 2008 at the Delaware Research and Technology Center in the Process Control & Logistics group
3
Cryogenic Air Separation
4
Motivations
• Energy Intensive– Air Liquide consumed more than 0.1% of the world’s electricity in
2010
• Dynamic Operating Environment– Energy price– Customer demands– Plant and Ambient Conditions
Compressor LimitsP
ower L
imits
Pip
elin
e P
ress
ure
Liquid Demand
GO
X D
eman
d
GA
N D
eman
d
Operator’s Preferred
Operating Region
“Sweet Spot”Optimized operating point considering all constraints and maximizing throughput
5
Plant Control System
RTO Technical Solution
Optimal setpoints
Problem to solve
Implement best setpoints and ramp the plant
Actual process and pipeline values
Target and schedule setup
Real Time Information
Customer demand
Energy price
Process Model
Predefined
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DCS
RTO IT Structure
Optimal Set Points
Ramp the PlantActual Process Values
Real Time Information
Process Model
Predefined
Air Separation
Unit
Real Time Optimizer
OPC Server APC
Expert System
Energy Price
Real Time Value Customer
Demand
Real Time Value
7
RTO Project Workflow
• Step 1: Plant Evaluation and Project Justification – KPI– Operating Environment
• Step 2: Scope Definition– Degrees of Freedom– Identifying Manipulated Variables
• Step 3: Plant Modeling– Controlled Variables, Objective Function, Constraints
• Step 4: Offline Optimization– Selection of Optimization Solvers
• Step 5: Online Implementation– Configuring Sampling Time, Solving Frequency, etc.– Designing Expert System– Connecting to DCS through OPC
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Collaboration of a Cross-function Team
• Sponsor/Management– Project Justification
• Process Expert– Scope Definition– Process Modeling
• Operations– Expert System– Online Implementation
• Optimization Expert– Selection of Optimization Solvers– Model Configuration and Debugging
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Case Study
C 2
D 2
C 1
ASU 1
ASU 2
K 1K-2
LOX Storage Tank
LAR Storage Tank
LIN Storage Tank
GOX
GAN
Compressed Air
P-6MV1
MV2
MV3
MV4
MV5 MV6
MV7
MV8
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Plant Model
• Manipulated Variables– Air flow rate to the ASU I (MV1)
– GOX production rate of ASU I (MV2)
– Compressed air production rate of ASU I (MV3)
– LIN production rate of ASU I (MV4)
– Air flow rate to the ASU II (MV5)
– LIN production rate of ASU II (MV6)
– The status of the turbine (MV7)
– The flow rate through the turbine if it is on (MV8)
• Objective Function( ) ( )
( ) ( ) ( )
⋅+−⋅++⋅++
⋅+⋅++⋅+
eIIIairIIairIairGOXIIGOXIGOX
LARILARLINIILINILINLOXIILOXILOX
PkkPQQPQQ
PQPQQPQQ
,,,,
,,,,,max
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Model Equations
• Controlled Variables (CV)– Mass Balance
– Regression from Historical Operation Data
IaircustomerairIIair
IGOXcustomerGOXIIGOX
QQQ
QQQ
,,,
,,,
−=−=
( )( )
( )( )( )IIairII
I
ILAR
IIGOXIILOX
ILOX
QMVfk
MVMVfk
MVMVfQ
MVMVMVQMVfQ
MVMVMVfQ
,55
314
213,
876,52,
4211,
,
,
,
,,,,
,,
==
===
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Optimization Features and Online Implementation
• Optimization Features– Mixed Integer Nonlinear Programming (MINLP)
– Solver: AOA of AIMMS
– Nonconvex– Multi-start technique of AIMMS for global optimization
• Online Implementation– Model Configured in OnOpt– Connected to DCS through MatrikonOPC Data Calculator as the
expert system
• Performance– Both solver and communication are robust– Savings have been observed and are being evaluated
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Conclusions
• Real time optimization can increase an air separation plant’s gross margin in a dynamic environment
• Investment is mainly software license and manpower• Cross functional team is needed. • The methodology can be easily applied to other process
plants