separation systems design under...
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LPPD LPPD Jamie Polan, LPPD ◦ Chicago, Illinois ◦ REU, Summer 2006
Separation Systems Design Under Uncertainty
Separation Systems Design Separation Systems Design Under UncertaintyUnder Uncertainty
Final Presentation for REU programAugust 3, 2006
Jamie PolanAdvisors: Professor Linninger
Andrés Malcolm
Laboratory for Product and Process DesignLaboratory for Product and Process DesignUniversity of Illinois- Chicago
Department of Chemical Engineering
LPPD LPPD Jamie Polan, LPPD ◦ Chicago, Illinois ◦ REU, Summer 2006
MotivationMotivation
• Must consume less energy to reduce dependence on foreign sources
• Could start by decreasing amount of energy in industry (33%)– chemical industry (24%)– petroleum refining industry (10%)
• Separations is a large part of the energy expense of these industries (60%)
Total energy production and consumption 1970 – 2025 (quadrillion Btu)
http://www.eia.doe.gov/oiaf/aeo/production.html
Residential -21%
Industrial -33%Commercial -18%
Transportation -28%Residential - 21%
Industrial - 33%Commercial - 18%
Transportation - 28%
U.S. Energy Consumption by Sector (2004)
http://www.eia.doe.gov/emeu/aer/consump.html
LPPD LPPD Jamie Polan, LPPD ◦ Chicago, Illinois ◦ REU, Summer 2006
Why Distillation?Why Distillation?
• 95% of separations energy used for distillation
• More than 6% of total U.S. energy consumption used for distillation
• Total consumption by U.S.94.27 quadrillion Btu
• Consumption by distillation6.03 quadrillion Btu
• If process improved 0.1%, we could save– 6.03 x 1012 Btu
– Over 1 million barrels of crude oil
–– $$70 million/year70 million/year http://news.bbc.co.uk/1/hi/sci/tech/4669260.stm
LPPD LPPD Jamie Polan, LPPD ◦ Chicago, Illinois ◦ REU, Summer 2006
DistillationDistillation
• Common method of separation in chemical and petroleum refining industries
• Uses concept of relative volatility
– tendency to vaporize– more volatile = higher vapor
pressure at operating temperature
• Very energy intensive– 40,000 distillation columns– >200 different processes
• Optimizing process will increase efficiency & minimize energy waste
http://www.energyinst.org.uk/education/coryton/page7.htm
LPPD LPPD Jamie Polan, LPPD ◦ Chicago, Illinois ◦ REU, Summer 2006
How to Improve the Process of DistillationHow to Improve the Process of Distillation
• Steady state process behavior is well-known; less is known about performance of dynamic systems
• Study the dynamic process to see how uncertainty affects – performance
– cost
– energy required
• Show effects of uncertainty with integrated design and control
• Considering control is necessary to perform a realistic uncertaintyanalysis
More energy efficient columns in real life situations by considering
the effects of dynamics and uncertainty in the design stage
LPPD LPPD Jamie Polan, LPPD ◦ Chicago, Illinois ◦ REU, Summer 2006
Distillation ColumnDistillation Column
Rectifying
Section
Stripping
Section
Purify light component
Purify heavy component
(prevent light component loss to bottoms product)
V
L
Feed
Boilup
Reboiler
BottomsProduct
Reflux
Condenser
Distillate
LPPD LPPD Jamie Polan, LPPD ◦ Chicago, Illinois ◦ REU, Summer 2006
Total amount in
LN-1xN-1 + VN+1yN+1
Material Balance= Total amount out
= LNxN + VNyN
yN = α xN
1 + (α – 1) xN
Vapor Liquid Equilibrium
Tray BalanceTray Balance
LN-1xN-1
LNxN VN+1yN+1
VNyN
LPPD LPPD Jamie Polan, LPPD ◦ Chicago, Illinois ◦ REU, Summer 2006
Case Study Case Study –– Dynamic Column ModelDynamic Column Model
• Components– hexane
– p-xylene
• Inputs– Temperature - 70°F– Pressure – 1 atm (1.01325 bar)– Feed rate basis – 5 kmol/hr– Feed compositions
» zh = 0.4» zx = 0.6
• Aim: Using Matlab, study how different sources of uncertainty affect the operation and cost of a dynamic column
Reflux
Feed
Condenser
Distillate
Boilup
ReboilerBottoms Product
LPPD LPPD Jamie Polan, LPPD ◦ Chicago, Illinois ◦ REU, Summer 2006
Controlled ColumnControlled Column
ControlControlFeed
B
Reflux D
Boilup0 1 0 0 0 2 0 0 0 3 0 0 0
0 .9 9 7
0 .9 9 8
0 .9 9 9
xd
m e a s u re dp re d ic te ds e t p o in t
0 1 0 0 0 2 0 0 0 3 0 0 0
0 .0 0 5
0 .0 1
0 .0 1 5
T im e S tep
xb
identification Identification + control
0 1 0 0 0 2 0 0 0 3 0 0 07 . 7 2 5
7 . 7 3
7 . 7 3 5
7 . 7 4
Boi
lup
0 1 0 0 0 2 0 0 0 3 0 0 0
1 . 0 6
1 . 0 8
1 . 1
Ref
lux
T im e S t e p
identification Identification + control
LPPD LPPD Jamie Polan, LPPD ◦ Chicago, Illinois ◦ REU, Summer 2006
Sensitivity Studies of Design and Uncertainty Sensitivity Studies of Design and Uncertainty Effects on CostEffects on Cost
• Changing design affects mainly capital cost– More trays, higher capital cost
• Uncertain process parameters affect operating cost– Feed composition
» Lower zF, higher operating cost– Relative volatility
» Lower α, higher operating cost
• Uncertainty in both zF and α leads to significantly higher costs– Avg. operating cost variation - $3,700– 40,000 columns– Over $135 million total
• This cost could be diminished if these sources of uncertainty were considered while designing columns
LPPD LPPD Jamie Polan, LPPD ◦ Chicago, Illinois ◦ REU, Summer 2006
Conclusions/Conclusions/Future DirectionsFuture Directions
• Improve operating cost model– better correlation of cost to energy requirements
• Develop systematic design methodology– predict column performance, cost, & energy requirements
when many variables are uncertain
• Study other parameter variations– heat quality
– tray efficiency
http://www.eia.doe.gov/kids/energyfacts/
• More energy efficient columns can be obtained by considering theeffects of dynamics and uncertainty in the design stage
• Design & uncertainty greatly affect costs
– More trays - higher capital cost
– Feed less concentrated in light component - higher operating cost
– Lower relative volatility – higher operating cost
• Many types of uncertainty should be studied simultaneously
– With both lower zF and α, millions of dollars are lost to uncertainty
LPPD LPPD Jamie Polan, LPPD ◦ Chicago, Illinois ◦ REU, Summer 2006
ReferencesReferencesAnnual Energy Outlook 2003. Energy Information Association, DOE, 2003.
http://tonto.eia.doe.gov/FTPROOT/forecasting/0383(2003).pdfBansal, Vikrant, John D. Perkins, and Efstratios N. Pistikopoulos, A Case Study in Simultaneous
Design and Control Using Rigorous, Mixed-Integer Dynamic Optimization Models, Ind. Eng. Chem. Res. 2002, 41, 760-778.
Bequette, B. Wayne, Process Dynamics: Modeling, Analysis, and Simulation, Prentice Hall PTR, New Jersey, 1998.
Doherty, M.F. and M.F. Malone. Conceptual Design of Distillation Systems. McGraw-Hill, 2001.Douglas, James M. Conceptual Design of Chemical Processes. McGraw Hill, 1988.Eldridge, R. Bruce and A. Frank Seibert. Hybrid Separations/Distillation Technology: Research
Opportunities for Energy and Emissions Reduction, 2005.Energy and Environmental Profile of the U.S. Petroleum Refining Industry. U.S. DOE/OIT, December
1998. http://www.eia.doe.gov/emeu/mecs/iab98/petroleum/sector.htmlFelder, Richard M. and Ronald W. Rousseau. Elementary Principles of Chemical Processes, 3rd ed.
John Wiley & Sons, New York, 2000.Malcolm, A. and A. A. Linninger, Integrating systems design and control using dynamic flexibility
analysis, submitted to I&ECR, 2006.Manufacturing Energy Consumption Survey. Energy Information Administration, DOE, 2001.
http://www.eia.doe.gov/emeu/mecs/iab98/petroleum/energy_use.htmlNIST – National Institute of Standards and Technology – Chemistry WebBook Database. Accessed
Wed, May 31, 2006.Perry, Robert H., Don W. Green. Perry's Chemical Engineers' Handbook. The McGraw-Hill Companies,
Inc., 1999.Seader, J.D. and Ernest J. Henley. Separation Process Principles. John Wiley & Sons, Inc., New York,
1998.World Crude Oil Prices. Energy Information Association, DOE, 2006.
http://tonto.eia.doe.gov/dnav/pet/pet_pri_wco_k_w.htm
LPPD LPPD Jamie Polan, LPPD ◦ Chicago, Illinois ◦ REU, Summer 2006
AcknowledgementsAcknowledgements
• Supplement from NSF DMI 0328134 “CLEAN CHEMICAL MANUFACTURING UNDER UNCERTAINTY”PI Andreas A. Linninger
• Professor Andreas Linninger
• Andrés Malcolm
• LPPD
http://www.schoolscience.co.uk/petroleum/index.html