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MULTIPERIOD DESIGN OF AZEOTROPIC SEPARATION SYSTEMS. Kenneth H. Tyner and Arthur W. Westerberg. OVERVIEW. Problem Description Problem Challenges Previous Work Related Research Issues Solution Approach Conclusions. F1. F2. PROBLEM DESCRIPTION. B. Design An Optimal Separation Plant - PowerPoint PPT Presentation
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MULTIPERIOD DESIGN OFAZEOTROPIC SEPARATION
SYSTEMS
Kenneth H. Tyner
and
Arthur W. Westerberg
OVERVIEW
• Problem Description
• Problem Challenges
• Previous Work
• Related Research Issues
• Solution Approach
• Conclusions
PROBLEM DESCRIPTION
• Design An Optimal Separation Plant
• Multiple Feeds– Flowrate
– Composition
– Operating Time
• Azeotropes
A
B
CAz
F1
F3F2
PROBLEM DESCRIPTION
A
B
CAz
F1
F3F2
F
A
B
C
Az
PROBLEM DESCRIPTION
A
B
CAz
F1
F3F2
F
A
B
C
PROBLEM DESCRIPTIONFEED 1 FEED 3FEED 2
PROBLEM DESCRIPTIONFEED 1 FEED 3FEED 2
PROBLEM DESCRIPTIONFEED 1 FEED 3FEED 2
PROBLEM DESCRIPTIONFEED 1 FEED 3FEED 2
PROBLEM DESCRIPTIONFEED 1 FEED 3FEED 2
PROBLEM CHALLENGES• Highly Combinatorial
– Separation Pathways– Process Units– Task Assignment
• Difficult Subproblems– Large Models– Highly Nonlinear– Recycle Streams– Shared Equipment
MULTIPERIOD DESIGN
• Constraints:– Column Dimensions
– Heat Exchanger Dimensions
– Flooding Conditions
MULTIPERIOD DESIGN
• Collocation Models:– Number of Trays and
Feed Location Variable
– Variable Transformations
MULTIPERIOD DESIGN
0.5
0.6
0.7
0.8
20 25 30 35 40
Trays
Fee
d L
oc
EXTEND TO AZEOTROPIC MULTIPERIOD DESIGN?
• Additional Feasibility Constraints
• How Many Columns?• Large Number of Simulations
• Stream Characteristics Change
INITIAL RESEARCH THRUSTS
• Synthesize Designs
• Evaluate Designs
• Optimize / Modify Designs
AZEOTROPIC SYNTHESIS
A
B
CAz
F
F
A
B
C
Az
AZEOTROPIC SYNTHESIS
A
B
CAz
F
A
B
C
Az
F
AZEOTROPIC SYNTHESIS
A
B
CAz
F
F
A
B
C
SIMULATION
ZeroSlack
S
S
S
SIMULATION
Solve / Optimize
Initialize
ModifyLibrary
REVISED RESEARCH THRUSTS
• Collocation Error Detection
• Scaling
• Solver Design
SIMULATION
Solve / Optimize
Initialize
ModifyLibrary
SOLUTION APPROACH
• Approximation– Separation Task– Column Design and Operation
• Shortcut Costing
• Autonomous Agents
ECONOMICS
Cost = F( Feed, Distillate, Trays, Reflux )
ECONOMICS
Cost = F( Feed, Distillate, Trays, Reflux )
Separation TaskContribution
ECONOMICS
Cost = F( Feed, Distillate, Trays, Reflux )
Separation TaskContribution
Column Design and OperationContributions
TASK APPROXIMATION
• Variables:– Compositions
– Flowrates
• Relations:– Mass Balance
– Lever Rule
– Geometric Objects
A
B
CAz
F
D / F
D
B
COLUMN APPROXIMATION
• Cost = F(Feed, Distillate, Trays, Reflux)
• Reflux = F(Trays, Feed Location)
COLUMN APPROXIMATION
• Cost = F(Feed, Distillate, Trays, Reflux)
• Reflux = F(Trays)
• Optimal Feed Location = F(Trays)
COLUMN APPROXIMATION
• Reflux = C1 * exp(-C2 * Trays) + C3
• Opt Feed Loc = C4 * Trays + C5
– Numerical Difficulties
• Gilliland Correlation
DATA COLLECTION
• Fix Trays and Task• Find Optimal Reflux
0
2
4
6
8
0 0.2 0.4 0.6 0.8
Feed Location
Ref
lux
DATA COLLECTION
8
10
12
14
16
18
20
22
30 35 40 45 50 55 60 65
Trays
Ref
lux
00.050.10.150.20.250.30.350.40.450.5
Fee
d L
ocat
ion
DATA COLLECTION
A
B
CAz
Store InDatabase
CalculateParameters
SIMULATION
F
A
B
C
Az
A
B
CAz
F
Database
SIMULATION
F
A
B
C
Az
A
B
CAz
F
Database
SIMULATION
ZeroSlack
S
S
S
ASYNCHRONOUS TEAMS
• Independent Software Agents
• Shared Memory
Trial Points
Newton Solver Gradient Solver
ASYNCHRONOUS TEAMS
• Independent Software Agents
• Shared Memory
Trial Points
Newton Solver Gradient Solver
ASYNCHRONOUS TEAMS
• Independent Software Agents
• Shared Memory
Trial Points
Newton Solver Gradient Solver
ASYNCHRONOUS TEAMS
• Independent Software Agents
• Shared Memory
Trial Points
Newton Solver Gradient Solver
ASYNCHRONOUS TEAMS
• Independent Software Agents
• Shared Memory
• Advantages– Scalable– Ease of Creation / Maintenance– Cooperation
ASYNCHRONOUS TEAMS
• Applications– Train Scheduling– Travelling Salesman Problem– Building Design
ASYNCHRONOUS TEAMS
ProblemDescription
ApproximationData
Designs
Database
DesignAgents
ApproximationAgents
MINLP DESIGN AGENT
• Fixed:– Separation Pathways– Intermediate Streams
• Variable:– Task Assignment – Number of Columns– Column Dimensions– Operating Policy
MINLP DESIGN AGENT
• Fixed:– Separation Pathways– Intermediate Streams
• Variable:– Task Assignment– Number of Columns– Column Dimensions– Operating Policy
MINLP DESIGN AGENT
• Fixed:– Separation Pathways– Intermediate Streams
• Variable:– Task Assignment– Number of Columns– Column Dimensions– Operating Policy
TASK ASSIGNMENT
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
20 30 40 50 60
Trays
Dia
mete
r
TASK ASSIGNMENT
$500,000.00
$600,000.00
$700,000.00
$800,000.00
$900,000.00
$1,000,000.00
$1,100,000.00
1 2 3 4 5 6 7
PATH SELECTION
• Sequential Selection
• Genetic Algorithm
• Active Constraint
MINLP DESIGN AGENT
• Fixed:– Separation Pathways– Intermediate Streams
• Variable:– Task Assignment– Number of Columns– Column Dimensions– Operating Policy
ASYNCHRONOUS TEAMS
ProblemDescription
ApproximationData
Designs
Database
DesignAgents
ApproximationAgents
GENERAL BENEFITS
• Alternative to Hierarchical Design
• Persistent Data
• Scenario Analysis
• Human Agents
MULTIPERIOD DESIGN OFAZEOTROPIC SEPARATION
SYSTEMS
Kenneth H. Tyner
and
Arthur W. Westerberg
