Pyosyn flexible platform for conceptual...
Transcript of Pyosyn flexible platform for conceptual...
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Pyosyn – flexible platform for conceptual designIgnacio Grossmann, Qi Chen
IDAES Stakeholder Meeting, May 16, 2019
Why Conceptual Design?
▪ Determine optimal process configuration
• Given inputs and desired outputs
▪ Answer strategic process investment questions:
• What process flowsheet should we select for the new new facility or plant?
• Is this new process technology worth our investment?
• How can we overcome process bottlenecks?
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Motivations for Conceptual Design
Process Intensification
▪ Intensification is “any
chemical engineering
development that leads to a
substantially smaller,
cleaner, and more energy-
efficient technology” [1]
▪ Prominent examples:
• Reactive distillation
• Dividing wall columns
• Rotating packed bed
• Microreactors
Traditional
▪ Competitive advantage
through cost-effective design
▪ Increased production rate
▪ Guide research and
development towards
maximum impact directions
Modular Manufacturing
▪ Modular design involves
partition of the system into
multiple easily
interconnected, self-
contained units (skids)
• “Numbering up” instead of
scaling up
▪ Main benefits:
• Reduced investment risk
• Improved time to market
• Increased flexibility
• Improved safety
• Reduced on-site construction
[1] Stankiewicz & Moulijn, 2000
Process Design Studies – Status Quo
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• Extensive search space ✓
• Realize synergies between processes ✓
• Simple input/output models
• Performance prediction maybe erroneous
• No commercial tool; mostly academic
Techno-economic Studies
• Detailed steady-state models ✓
• Reasonable cost estimates ✓
• Not extensive, case by case analysis
• Difficult to realize synergistic advantages
• More a sensitivity study
Validate design
Update model
Conceptual Design Studies
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II
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III
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III
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Need for Automated Computational Tools
Evaluating process alternatives
State of the art
▪ Trial and error in commercial simulators
▪ Heuristic rules of thumb (e.g. PROSYN) [1]
Industry
Conceptual Design
[1] Schembecker et al., 1994
𝟐𝟒 = 𝟏𝟔 flowsheets
Can optimize Superstructure to avoid enumeration
Commercial process simulators
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Superstructure-based Conceptual Design
3. Solve synthesis problem
▪ Solve mathematical program
to obtain optimal flowsheet
configuration and operating
conditions
• Equipment selection and
interconnection
• Flows, temperatures,
pressures
▪ Various commercial and
academic solver codes
available
1. Define process alternatives
▪ Postulate a superstructure
which represents all
practical alternatives
• Existing, proposed,
and/or hypothetical
process technologies
2. Symbolic-algebraic representation
▪ Formulate an mathematical
model that captures the
design problem logic
▪ Normally a mixed-integer
nonlinear programming
(MINLP) or generalized
disjunctive programming
(GDP) problem
min 𝑍 = 𝑓 𝑥, 𝑦
s.t. ℎ 𝑥, 𝑦 = 0
𝑔 𝑥, 𝑦 ≤ 0
𝑥 ∈ 𝑋, 𝑦 ∈ 0,1 𝑚
( )
Ω
,0)(
0)(
)(min
1
falsetrue,Y
Rc,Rx
trueY
K k
γc
xg
Y
Jj
xs.t. r
xfc Z
jk
k
n
jkk
jk
jk
k
kk
=
=
+=
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Challenges/barriers
There is no commercial software for synthesis
Only academic codes, some prototypes PROSYN [1], ICAS [2], MIPSYN [3]
Lack of robustness of nonlinear optimization (NLP, MINLP)
Difficulties with convergence:
Good initialization required; zero flows cause singularities; nonconvexities
give rise to local optima
However significant progress has been made
Synthesis tools require expert users
How to postulate superstructure? How to develop best computational strategy?
Unclear how to address Process Intensification, Modular Design
Increased demand for synthesis of new flowsheets
Shale gas revolution => many new plants in US
325 projects announced since 2010 $194 billion (American Chemistry Council)
New driving force
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Pyosyn – Conceptual Design in IDAES
IDAES Framework
Pyosyn
process synthesis
ALAMO (data →thermodynamics)
RIPE (data → kinetics)
IDAES Model Library (standard unit models)
Detailed simulation
Visualization
Dynamic optimization
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Pyosyn central principles
▪ Intuitive modeling contexts
– Generalized Disjunctive Programming (GDP)
• Prototype logical expression system
• Implication, Equivalence
– Pyomo.Network
▪ Flexible solution approaches
– MINLP reformulation
– Logic-based decomposition algorithms
– Logic-based relaxation tightening (basic steps)
Ports figure adapted from Menezes et al., 2015
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Example: Kaibel column conceptual design
▪ Intensified distillation column
– One column shell, 4 product separations
– Variant of the dividing wall column
▪ Objectives:
– Attain product quality specifications
– Minimize capital and operating costs
• Total annualized cost
▪ Major design decisions:
– Number of trays in each section
– Feed tray selection
– Product (R1, R2) tray selection
– Reboiler and condenser duties
– Liquid/vapor distributor ratios (fixed)
▪ Steady state tray-by-tray MESH model
– Mass balance, equilibrium, summation
(conservation), enthalpy (H) balances
Figure from Rawlings et al., 2019
For up to 58 trays
42 million combinations
of feed and products tray location
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Superstructure column distillation model
Condenser Tray
(permanent)
Rectification Trays
(conditional)
Feed Tray
(permanent)
Stripping Trays
(conditional)
Reboiler Tray
(permanent)Heavy Product
Feed
Light
Product
}
-OR-
-OR-
-OR-
-OR-
}Vapor Flow
Liquid Flow
Equilibrium Stage
Non-equilibrium Stage
•Permanent and conditional trays:
MESH equations for condenser,
reboiler and feed trays
Mass & energy balances for
rectification and stripping trays.
•Conditional trays only:
Use disjunctions as modeling tool
If Yn=True apply VLE constraints
OR Yn=False NO VLE
Disjunctions for existence/absence of conditional trays
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Kaibel column conceptual design
▪ Components: Methanol, ethanol, n-propanol,
n-butanol
– 99% purity for each component
▪ GDP model written using Pyomo.GDP
– 5715 constraints
• 2124 nonlinear
– 100 disjunctions
• 3599 variables
– 178 binary
– 3421 continuous
▪ Solved in 639 sec using GDPopt-LOA solver
– Logic-based outer approximation algorithm
– 4 iterations
▪ Resulting design:
– 46 trays (21% reduction vs. base case)
– Dividing wall between 12th and 26th tray
– Feed at 18th tray
– Side outlets at 13th and 22nd trays
ABCD
A
B
C
D
Optimal Design Kaibel Column reduces energy consumption in the reboiler
and condenser by more than 40 % compared to conventional columns
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Effective Generalized Disjunctive Programming
models for Modular Plant Design
• Maximize Net Present Value (NPV) profit
– Sales revenue
– Raw material costs
– Production costs
– Investment costs (related to facility size)
– Transportation costs (raw materials, products, and modules)
• Discrete decisions
– Selection of sites
– Selection of modules
– Selection of transportation links
• Continuous decisions
– Production levels at each site
– Shipment quantities along each link
GDP -> MINLP (big-M or hull reformulation)
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Modular standardization
continuous decisions ⟶ discrete decisions
NLP ⟶ MILP
Special structure: rewrite nonlinear relations as mixed-
integer linear
Example: batch processing*
𝐶𝑜𝑠𝑡 =
𝑖
𝛼𝑖𝑆𝑖0.6
If each reactor available in discrete sizes 𝑠1, 𝑠2, 𝑠3,
then we can write
𝐶𝑜𝑠𝑡 =
𝑖,𝑗
𝑘𝑖𝑗𝑦𝑖𝑗
𝑘𝑖𝑗 = 𝛼𝑖𝑠𝑖0.6 constant
𝑦𝑖𝑗 ∈ 0, 1
for all reactors 𝑖 and size options j ∈ {1, 2, 3}Equivalent to basic step with hull reformulation on
disjunction between sizes [3]
*Grossmann et. al., 1992; Voudouris & Grossmann, 1991
Induced linearity reformulation
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Case study: multiple market capacity expansion
• Given:
– 5 distributed customer markets
– time-varying demand (10 year horizon)
• Optimize:
– Facility locations, size, product shipment quantities/routes
– Minimize system cost to satisfy demand
• Allow relocation of modules between sites
Nonlinear GDP:
2224 variables (718 integer), 1387 constraints, 13 disjunctions
Used big-M reformulation.
MINLP solved in 7s DICOPT solver
via Pyomo-GAMS solver interface and GAMS version 25.1.3.
Demands
Location Markets
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Case study: multiple market capacity expansion
Results
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Concluding Remarks
Conceptual design allows the systematic examination of many design alternatives
Superstructure-Modeling-Solution paradigm is the basis of modern conceptual design tools
Progress in representations and discrete/continuous models and algorithms major driver for new tools
Scope can be expanded to process intensification and modular designs
Pyosyn: new unique state-of-the-art IDAES tool can accomplish four points listed above
See poster: Advanced Tools for Conceptual Design (Qi Chen/Michael Bynum)
Disclaimer This presentation was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
idaes.org
We graciously acknowledge funding from the U.S. Department of Energy, Office of Fossil Energy,
through the Crosscutting Research Program and the Advanced Combustion Systems Program.
The IDAES Technical Team: • National Energy Technology Laboratory: David Miller, Tony Burgard, John Eslick, Andrew Lee, Miguel Zamarripa,
Jinliang Ma, Dale Keairns, Jaffer Ghouse, Emmanuel Ogbe, Gary Kocis, Ben Omell, Chinedu Okoli, Richard Newby,
Grigorios Panagakos, Maojian Wang
• Sandia National Laboratories: John Siirola, Bethany Nicholson, Carl Laird, Katherine Klise, Dena Vigil, Michael
Bynum, Ben Knueven
• Lawrence Berkeley National Laboratory: Deb Agarwal, Dan Gunter, Keith Beattie, John Shinn, Hamdy Elgammal,
Joshua Boverhof, Karen Whitenack
• Carnegie Mellon University: Larry Biegler, Nick Sahinidis, Chrysanthos Gounaris, Ignacio Grossmann, Owais Sarwar,
Natalie Isenberg, Chris Hanselman, Marissa Engle, Qi Chen, Cristiana Lara, Robert Parker, Ben Sauk, Vibhav
Dabadghao, Can Li, David Molina Thierry
• West Virginia University: Debangsu Bhattacharyya, Paul Akula, Anca Ostace, Quang-Minh Le
• University of Notre Dame: Alexander Dowling, Xian Gao
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For more information visit us at: https://idaes.org/
David C. Miller, Ph.D.
Technical Director
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Pyosyn flexible solution strategies
▪ Vision: write model once, try several solution strategies
GDP model
𝑔1 𝑥 ≤ 0∨
𝑔2 𝑥 ≤ 0
Big-M
HR
Cutting planes
MINLP model𝑔 𝑥, 𝑦 ≤ 0
Basic Step
GDP solvers
MINLP solvers
Model Solution
Model Reformulation Solver
GDP to MINLP reformulations
▪ Standard reformulations to MINLP▪ Big-M Reformulation (BM)
▪ Hull Reformulation (HR)
▪ Advanced reformulations to MINLP▪ Cutting-plane based hybrid BM/HR
reformulation
▪ Single-line automatic model reformulation
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Pyosyn flexible solution strategies
▪ Vision: write model once, try many solution strategies
GDP model
𝑔1 𝑥 ≤ 0∨
𝑔2 𝑥 ≤ 0
Big-M
HR
Cutting planes
MINLP model𝑔 𝑥, 𝑦 ≤ 0
Basic Step
GDP solvers
MINLP solvers
Model Solution
Model Reformulation Solver
GDP to MINLP reformulations
▪ MindtPy decomposition-based MINLP solver▪ Open source python implementation
▪ Traditional outer approximation
▪ Feasibility Pump
▪ Level-based outer approximation
▪ Interface to commercial solvers▪ Via GAMS
▪ Via AMPL
NEW
NEW
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Pyosyn flexible solution strategies
▪ Vision: write model once, try many solution strategies
GDP model
𝑔1 𝑥 ≤ 0∨
𝑔2 𝑥 ≤ 0
Big-M
HR
Cutting planes
MINLP model𝑔 𝑥, 𝑦 ≤ 0
Basic Step
GDP solvers
MINLP solvers
Model Solution
Model Reformulation Solver
GDP to MINLP reformulations
▪ GDPopt solver
▪ Logic-based outer approximation
▪ Global logic-based outer approximation
▪ MC++ interface – McCormick envelope computation
▪ Disjunctive range reduction using feasibility-based bounds tightening or optimality-based bounds tightening
▪ GDPbb solver
▪ Nonlinear disjunctive branch and bound
▪ Z3 satisfiability solver interface –infeasible node screening
NEW
NEW
NEW
NEW
NEW
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Pyosyn flexible solution strategies
▪ Vision: write model once, try many solution strategies
GDP model
𝑔1 𝑥 ≤ 0∨
𝑔2 𝑥 ≤ 0
Big-M
HR
Cutting planes
MINLP model𝑔 𝑥, 𝑦 ≤ 0
Basic Step
GDP solvers
MINLP solvers
Model Solution
Model Reformulation Solver
GDP to MINLP reformulations
▪ Basic step operation▪ Improves linear approximation of GDP
model at expense of model size