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BIOFUEL PRODUCTION PROCESSES BASED ON
SYSTEMATIC OPTIMIZATION METHODOLOGIES
September 18th, 2013Coimbra, Portugal
José F.O. [email protected]
GEPSI – PSE Group, CIEPQPFChemical Engineering Department
University of Coimbra, Portugal
Nuno M.C. [email protected]
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
Presentation outline
• Motivations
• Project framework
• Some work developed
Modelling & parameter estimation of LLE and VLE systems
Sodium methylate production process. Simulation and analysis
Optimal design of solid-liquid extraction units
• End notes
(4/53)
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
MOTIVATIONS
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
5/53
Economy based on fossil resourcesMOTIVATIONS
RAW MAT E RIAL S INT E RMEDIATESP R ODUCT S/
U S E SCOM M OD ITI E S
S E CONDAR YCOMM ODITIE S
UPSTREAM REFINERY DEPLOYMENT&
DISTRIBUTIONFigure 1. Fossil-based refinery concept.
• Highly cost-efficient industries since the
upstream to the downstream steps.
• Broad number of products and uses.
• Well stablished technologies.
• Oil & gas combined global market value of
$2.6 trillion of dollars in 2010.
• Coal, gas & oil combined annual volume of
77 billion BOE spent in 2012.
• Coal market value is $600 billion of dollars
in 2010, more 14.5% than 2007.
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
6/53 MOTIVATIONS
RAW MATERIALS
Petroleum
Natural gas
Coal
Tar sands bituminous
Oil shales
COMMODITIES
Benzene
Gasoline
Diesel
Xylene
Toluene
Butanes
Ethane/Ethylene
Chlorine
CO / H2
O2/N2
SO2
SECONDARY COMMODITIES
Ethylene benzene
Cyclohexane
Cumene
P-Xylene
Iso-butylene
Butadiene
Ethylene oxide
Propylene
Ethylene Dichloride
Methanol
Ammonia
Sulphuric acid
Styrene
Adipic acid
Caprolactam
Phenol
Acetone
Terephthalic acid
Ethylene glycol
Propylene oxide
Acrylonitrile
Vinyl Chloride
Formaldehyde
MTBE
Acetic acid
Nitric acid
INTERMEDIATES
Polystyrene
Nylon 6,6, polyurethanes
Nylon 6
Phenol-formaldehyde resins, Bisphenol A,Caprolactam, Dalicylic acid
Methyl methacrylate, Solvents, Bisphenol A,Pharmaceuticals
Toluene diisocyanate, foam polyurethanes
MTBE
Polybutadiene, neoprene, styrenebutadiene rubber
Polypropylene, polypropylene glycol,propylene glycol
adiponitrile, acrylamide
Polyvinyl chloride
Urea-formaldehyde resins,phenol-formaldehyde resins
Oxygenated gasoline additive
Vinyl acetatePolyvinyl acetatePolyvinyl alcoholPolyvinyl butyral
Ammonium nitrate, adipic acid,fertilizers, explosives
Phosphate fertilizer, ammonium
PRODUCTS/USES
TEXTILS
coatings, foam cushions, upholstery,drapes, lycra, spandex
SAFE FOOD SUPPLYFood packaging, preservatives,fertilizers, pesticides, beverage
bottles, appliances, beverage cancoatings, vitamins
TRANSPORTATIONFuels, oxygenates, anti-freeze, wiper
belts hoses, bumpers, corrosioninhibitors
CONSTRUCTIONPaints, resins, siding, insulation,
retardents, adhesives, carpeting
RECREATIONFootgear, protective equipment,
tires, wet suits, tapes- CD’s-DVD’s,golf equipment, camping gear,
Rboats
COMMUNICATIONMolded plastics, computer casings,
displays, pens, pencils, inks, dyes,paper products
HEALTH & HYGIENEPlastics eyeglasses, cosmetics,
detergents, pharmaceuticals, suntanlotions, medical- dental products,
disinfectants, aspirin
Figure 2. A product flow-chart from petroleum feedstocks.
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
7/53 MOTIVATIONS
Figure 3. World total proved reserves of oil (BP, 2013).
• Geographic concentration of resources.
10.000 +
8. 000 - 9. 999
6. 000 - 7. 999
4. 000 - 5. 999
2. 000 - 3. 999
0 - 1.99 9
NO DAT A
(mtoe )
PROVEDRESERVES
Economy based on fossil resources
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
8/53 MOTIVATIONS
0
50
100
150
200
250
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Pric
ein
dexe
s(in
dex
2005
=10
0)
Year
Non-fuel
Industrialinputs
Fuel
Food
OPEC production
Asian l crisis
9/11
Iraq war
PDVSA strikeWeaker dollar
ArabSpring
Subprimemortgage crisis
Low spareproduction
0
25
50
75
100
125
150
175
200
225
250
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
R/P
(yr)
Year
CoalGasOil
Figure 4. Price indexes adjusted to inflation. Data from BP (2013). Figure 5. Reserves-to-production ratio of coal, gas and oil. Data from BP (2013).
• Geographic concentration of resources.
• Energy security and prices instabilities.
• Long-term supply shortcomings.
• Contributes to global-warming.
Economy based on fossil resources
“CO2 levels surpassed 400 ppm for the first time in
3 to 5 million years…”, “… a time where climate
was considerably warmer than it is today.” (BBC
News, May 10th, 2013).
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
9/53 MOTIVATIONS
Figure 6. Bio-economy concept.
Bio-economy
BI OMAS S
BIOREFINERIES
BIO-ENERGY
PRODUCT, FUEL AND ENERGY MARKETS
BIO-ECONOMY
BIO-PRODUCTS BIO-FUELS
• Market valorises bio-products.
• Global market value for bio-products increased from 2001 to 2012 from $20 billion to $200+ billion of dollars.
• Biofuels global market was $83+ billion in 2011 and is forecasted $185 billion of dollars for 2021.
BI OMAS S P R E CUR S OR SS E CONDAR YCH EM ICAL S
INT E R ME DIAT E SPRODUCT S
US E SINTER MED IAT E
P LAT FO R MSBUILDIN G
BL OCK S
• Develop bio-products mimicking functionalities of petroleum-based or improved.
• Valorise biomass regarded as waste.
Figure 7. Bio-economy concept.
DEPLOYMENTBIOREFINERYHARVESTING
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
10/53 MOTIVATIONS
BIOMASS PRECURSORS
Lignocellulosic materials(e.g. trees, barks, sawdust)
Cereals(e.g. corn, wheat)
Plant oils and animal fats(e.g. soybean, beef tallow)
Sugar and molasses(e.g. sugar cane, beets)
Green plants(e.g. grasses, algae)
Miscellaneousorganic wastes
Agricultural wastes(e.g. corn stover)
Carbohydrates
Starch
Hemicellulose
Cellulose
Lignin
Oil, fats
Protein
Bio-basedSyngas
SGC1
C2
C3
C4
C5
C6
Ar
DirectPolymers & Gums
Sugars
Glucose
Fructose
Xylose
Arabinose
Lactose
Sucrose
Starch
Hydrogen
Methanol
Mixed alcohols
Higher alcohols
Oxo-synthesisproducts
Iso-synthesisproducts
Fischer-TropschLiquids
Glycerol
Lactic acid
3-Hydroxyl--propionate
Propionic acid
Malonic acid
Serine
Succinic acid
Fumaric acid
Malic acid
Aspartic acid
3-Hydroxy--butyrolactone
Acetoin
Threonine
Itaconic acid
Furfural
Levulinic acid
Glutamic acid
Xylonic acid
Xylitol/Arabitol
Citric/Aconiticacid
5-Hydroxy--methyl-furfural
Lysine
Gluconic acid
Glucaric acid
Sorbitol
Gallic acid
Ferulic acid
Ammonia synthesis, hydrogenation products
Methyl esters, formaldehyde, acetic acid, dimethylether,
Linear and branched alcohols, and mixed higher alcohols
Iso-C4 molecules, isobutene and its derivatives
Fermentation products, propylene glycol, malonic, 1,3-PDO,diacids, propyl alcohol, dialdehyde, epoxides
Acrylates, L-Propylene glycol, Dioxanes, Polyesters, Lactide
Acrylates, Acrylamides, Esters, 1,3-Propanediol,Malonic acid and others
Reagent, propionol, acrylate
Pharma intermediates
2-amino-1,3-PDO, 2-aminomalonic, (amino-3HP)
THF, 1,4-Butanediol, γ-butyrolactone, pyrrolidones, esters,diamines, 3,3-bionelle, hydroxybutyric acid
Unsaturated succinate derivatives
Hydroxy succinate derivatives, hydroxybutyrolactone
Amino succinate derivatives
Hydroxybutyrates, epoxy-γ-butyrolactone, butenoic acid
Butanediols, butenols
Diols, ketone derivatives, indeterminant
Methyl succinate derivatives, unsaturated esters
Many furan derivatives
δ-aminolevullinate, 2-Methyl THF, 1,4-diols, esters, succinate
Amino diols, glutaric acid, substituted pyrrolidones
Lactones, esters
EG, PG, glycerol, lactate, hydroxy furans, sugar acids
1,5-pentanediol, itaconic derivatives, pyrrolidones, esters
Numerous furan derivatives, succinate, esters, levullinic acid
Caprolactam, diamino alcohols, 1,5-diaminopentane
Gluconolactones, esters
Dilactones, monolactones, other products
Glycols (EG, PG), glycerol, lactate, isosorbide
Phenolics, food additives
Fuel oxygentates
Reagents-building unit
Antifreeze and deicers
Solvents
Green solvents
Speciality chemicalsintermediate
Chelating agents
Amines
Plasticizers
Polyvinyl acetate
pH control agents
Resins, crosslinkers
Polyvinyl alcohol
Polyacrylates
Polyacrylamides
Polyethers
Polypyrrolidones
Phthalate polyesters
PEIT polymer
Polyhydroxypolyesters
Nylons (polyamides)
Polyhydroxypolyamides
Bisphenol A replacement
Polycarbonates
Polyurethanes
Phenol-formaldehyde resins
Polyhydroxyalkonoates
Polysaccharides
Polyaminoacids
INDUSTRIALCorrosion inhibitors, dust control,
boiler water treatment, gas
TRANSPORTATIONFuels, oxygenates, anti-freeze,
seats, belts hoses, bumpers,corrosion inhibitors
SAFE FOOD SUPPLYFood packaging, preservatives,fertilizers, pesticides, beverage
bottles, appliances, beverage cancoatings, vitamins
TEXTILS
coatings, foam cushions,upholstery, drapes, lycra, spandex
ENVIRONMENT
chelators, cleaners anddetergents
COMMUNICATIONMolded plastics, computer
liquid crystal displays, pens,pencils, inks, dyes, paper products
CONSTRUCTIONPaints, resins, siding, insulation,
cements, coatings, varnishes,
carpeting
RECREATIONFootgear, protective equipment,
& tires, wet suits, tapes-CD’s-DVD’s, golf equipment, camping
gear, boats
HEALTH & HYGIENEPlastics eyeglasses, cosmetics,detergents, pharmaceuticals,
suntan lotions, medical-dentalproducts, disinfectants, aspirin
INTERMEDIATEPLATFORMS
BUILDINGBLOCKS SECONDARY CHEMICALS INTERMEDIATES PRODUCTS/USES
Figure 8. A products flow-chart concept from biomass.
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
11/53 MOTIVATIONS
Bio-economy
• High spatial distribution of biomass resources and intermittent availability.
• Technological hindrances in the conversion of cheap feedstock (e.g. forest and agro wastes).
• Biomass morphology and chemical composition are highly variable.
• Intensification of biomass usage increases water demand.
• High uncertainty in the prediction of thermodynamic properties.
• Identification of the adequate product portfolio for the biorefinery.
Main challenges
PSE tools and know-how are being use to tackle above issues in biorefinery context.
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
12/53
* Scope of this work.
*
MOTIVATIONS
Figure 9. Decisions hierarchy in PSE (Grossmann, 2010).
More focus on process synthesis & analysis.
Decision-making with PSE tools
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
13/53
Figure 10. Academic & Industry perspectives (adapted from Neves, 2007).Major concerns in biofuel industry at single-site level are feedstock
costs, equipment cost, and energy and water consumptions.
MOTIVATIONS
Modelling(complexity)
Simulation
Optimisation(poor solutions)
Energy(costs )
Separation(efficiency )
Reaction(production )
PSE
Academic view(difficulties to overcome)
Industrial view(benefits to accomplish)
(large-scale)
Decision-making with PSE tools
(14/53)
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
PROJECT OVERVIEW
15/53
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
PROJECT OVERVIEW
T R ANSP ORT& HARVE STI NG STOR AGE SURGE B IN
& SCALEDESTONI NG DRYI NG
CR ACK ING,ASPIR ATION& DEHU LLI NG
HULLS &MILL FEED
CONDIT IONINGFLAKI NG MILL
SOLV ENT
SOLV ENTMARC
FLASHDESOLV EN-TIZ ING
B AGASSE
EXTR ACTI ON
SOLV ENTMAK EUP
Y EASTENZY MES
SOLV ENTMAK EUP
FLASH
EVAP ORA TO R
FLASH
Y EASTRECYCLE
GLYCEROL
PROTE INCONCEN TRATE
BIOETHANOL
WATER
WATER
2SEEDSPREPARATION
1COLLECTIN G& TRANSPORTING
3SOLV ENTEXTRACTI ON
4BIOETHANOLPROCESS
5BIODIESELPROCESS
MI SCE LLA
V EG. OIL
SACCHARI FICATION
MET HAN OL
LLEXT R ACTION
R EACTION
FER MENTATI ON
FLOUR
WORTFERMENTEDWORT
SEPAR ATI ON
OIL RECYCLE
BIODIESEL
SEPAR ATION
SEPAR ATION
WATER+ GLYCEROL
B IODIESEL+ OIL
FIBERS
SOY BE ANS
Biorefinery based on whole-crop biomassFigure 11. Whole-crop biorefinery based upon soy bean.
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
16/53 PROJECT OVERVIEW
Work performed within the project
• Modelling & parameter estimation of LLE and VLE systems.
• Kinetic studies of transesterification reaction for biodiesel production.
• Sodium methylate production process. Simulation and analysis.
• Optimal design of industrial solid-liquid extraction units.
(17/53)
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE
SYSTEMS
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
18/53 MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
• Study of water – ethanol – IL LLE ternary systems.
Application to ethanol purification.
• Modelling and parameter regression of VLE data for IL-water and IL-
ethanol binary pairs.
• Solubilities of ILs in water (ongoing).
• Parameter regression for single strong aqueous electrolytes.
Tasks accomplished
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
19/53
• Development of an alternative process to purify ethanol based on L-L extraction.
• 7 phosphonium-based ionic liquids were tested as potential solvents. Experimental data of water – ethanol – IL ternary systems was gathered. LLE modelling and parameter regression with NRTL model. LLE predictions with COSMO-RS.
P+
N-
S
O
OF
F
F
S
O
OF
F
F
O O-
P
O-O
-N
N N
SO
O
O-
[TDTHP]+
Cl- Br-
[Deca]-
[Phosph]- [CH3SO3]-
[N(CN)2]- [NTf2]-
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
* Join collaboration with PATh-CICECO group, University of Aveiro.
Study of water – ethanol – IL ternary systems
Figure 12. Molecular structures of all IL studied.
Detailed description of this work in Neves et al. (2011).
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
20/53
Figure 13. Local molecular clusters.
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
Study of water – ethanol – IL ternary systems• Modelling
Necessary condition for liquid-liquid equilibria.2
11
2
1
2
1
21
2
2
1
Molecule 1in centre
Molecule 2in centre
g21
g11g12 g22
NRTL
1, 2, ..., I IIi i i N
= 1, 2, ...,
I IIi i
I IIi i
i i i
f f
a a
a x i N
NRTL model (Renon, 1968) used to describe non-ideality.
lnc cE n n
i ii i
i i i
x Lgx
RT M
lncn
j ij jii ij
ji j j
x G LLM M M
cn
i k ki kik
L x G
cn
i k kik
M x G
ij ij
ijG e
( ), and 0, ij ii ij
ij ij
g g gi j i j
RT RT
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
21/53 MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
Study of water – ethanol – IL ternary systems• NRTL parameters regression
3 adjustable parameters per binary pair , , .ij ji ij ji
Problem easy to formulate and small, but can be hard to tackle due to its high non-linearity and non-convex nature.
NLP1
2 2exp modmin = ( )
. . ( , ) 0
1 0 1,2,... ; 1,2
0, 1,2,... ; 1,2,... ; 1,2
t cn n
ijk ijk ijkzi j k
ijk tj
L Uij ij ij
ijk t c
w w
s t NRTL x
x i n k
x i n j n k
NLP 1 implemented in GAMS and solved with CONOPT, OQNLP and BARON.
• Regression problem formulation:
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
22/53 MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
Study of water – ethanol – IL ternary systems• NRTL parameters regression
4th law of thermodynamics: "Anything that can go wrong, will go wrong."
Figure 14. Excess Gibbs energy curve of a binary mixture system. LLE example.
• After regression, stability tests must be done to avoid meaningless parameter values.
00 1.0
Δm GR T
X1
X1L1X1
L2
μ1L1
μ 1L2
00 1.0
Δm GR T
X1
X1L1X1
L3X1L2
F(y)
μ 1L1
μ 1L3
μ 1L2
Figure 15. Excess Gibbs energy curve of a binary mixture system. 3 phase LLE example.
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
23/53 MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
Study of water – ethanol – IL ternary systems• Stability test problem formulation
Minimization of F(y):
NLP2
min ( )=
. . (y; ) 0
1 0
0, 1,2,...
cnF
i i iyi
ii
i c
F y y y
s t NRTL
y
y i n
Phases are stable if and only if 0for all space of candidate phase with composition y.
1) Solve NLP1 with OQNLP.
2) Generate a pool of all local solutions found.
3) Test stability solving NLP2 for each experiment.
4) If all stable finish. Else, go to 3) with the 2nd best solution, etc.
• Numerical procedure adopted
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
24/53
Figure 17. Ternary phase diagram [TDTHP][Deca] + EtOH + H2O at 298 K (mass fraction units).
• NRTL parameters for seven water(1) – etanol(2) – Ionic liquid(3) ternary systems were obtained.
Figure 16. Ternary phase diagram [TDTHP][Phosph] + EtOH + H2O at 298 K (mass fraction units).
[TDTHP][Phosph]0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
EtOH
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
H2O
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
[TDTHP][Deca]0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
EtOH
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
H2O
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
Study of water – ethanol – IL ternary systems• Results
• A commercial package of COSMO-RS model is used to predict LLE. It uses quantum calculations coupled with statistical thermodynamic approaches.
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
25/53
Figure 19. Ternary phase diagram [TDTHP][CH3SO3] + EtOH + H2O at 298 K (mass fraction units).
Figure 18. Ternary phase diagram [TDTHP][Cl] + EtOH + H2O at 298 K (mass fraction units).
[TDTHP]Cl0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
EtOH
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
H2O
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
[TDTHP][CH3SO3]0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
EtOH
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
H2O
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
• Parameters 13, 31, 23 , 32 are adjusted while:
12 13 23 12 210 3031 0 2 0 3 670 4 55 2. , . , . , . / , . /T T
are fixed as suggested by Song and Chen (2009).
• Results
Study of water – ethanol – IL ternary systems
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
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Figure 21. Ternary phase diagram [TDTHP][N(CN)2] + EtOH + H2O at 298 K (mass fraction units).
• All systems are type I.
Figure 20. Ternary phase diagram [TDTHP][Br] + EtOH + H2O at 298 K (mass fraction units).
[TDTHP]Br0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
EtOH
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
H2O
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
[TDTHP][N(CN)2]0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
EtOH
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
H2O
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
Study of water – ethanol – IL ternary systems• Results
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Figure 22. Ternary phase diagram [TDTHP][NTf2] + EtOH + H2O at 298 K (mass fraction units).
[TDTHP][NTf2]0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
EtOH
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
H2O
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
System D SMaximum EtOHextraction (%)
[TDTHP]Cl 0.82 6.6 72
[TDTHP]Br 0.68 7.9 78
[TDTHP][NTf2] 0.07 22 87
[TDTHP][Phosph] 0.85 5.7 72
[TDTHP][Deca] 0.81 5.3 70
[TDTHP][N(CN)2] 0.51 7.8 82
[TDTHP][CH3SO3] 0.89 6.7 65
[TDTHP][B(CN)4] - - 91a
[TDTHP][C(CN)3] - - 80a
Table 1. Distribution coefficients and ethanol selectivities for each system at the lowest tie-line, and maximum ethanol concentration obtainable (mass basis).
a Predicted by COSMO-RS.
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
• Results
Study of water – ethanol – IL ternary systems
• Concentrations of up to 65% wt in ethanol can
be achieved from 2% wt ethanol feed, using a
single LL extraction stage.
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• Values of 0 at the optimum varied between 0.7×10-3 and 7×10-3 for systems Br- and [N(CN)2]- , respectively.
Ionic liquidNRTL binary interaction parameters
τ13 τ31 τ23 τ32
[TDTHP]Cl 11.14 -2.555 5.230 -3.181
[TDTHP]Br 21.09 6.265 4.688 -2.760
[TDTHP][NTf2] 11.36 4.674 4.798 -1.520
[TDTHP][Phosph] 25.25 -1.450 6.064 -3.917
[TDTHP][Deca] 23.82 -1.169 5.487 -3.559
[TDTHP][N(CN)2] 14.82 1.313 4.865 -2.873
[TDTHP][CH3SO3] 11.09 -3. 487 5.998 -3.318
Table 2. NRTL binary interaction parameters for each system at 298.15 K.
• Results
Study of water – ethanol – IL ternary systems
• Number of local optima varied between 5 and 77 for the systems [NTf2]- and [Br]-, respectively.
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
• F(y) varied between -1×10-10 and 0. Therefore all data points were considered stable for the best NRTL parameter set found.
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Pervaporation
VaporizerCooling
LLextractor
Fermenter
Feed
Watermakeup
Broth
RecycleResidue Solvent
Purge ILmakeup
Extract
Hydratedethanol
Anhydrousethanol
Waterresidue
Figure 23. Block diagram for ethanol purification based on liquid-liquid extraction and pervaporation.
• A LL extraction stage coupled to an extractive fermentation.
• IL is continuously recycled to the fermentator.
• Further ethanol concentration is carried out by pervaporation.
• This design applicable in other contexts, where ethanol is to be separated.
Study of water – ethanol – IL ternary systems• Alternative process for bioethanol purification
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
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• Characteristics of electrolyte solutions
• Modelling (single strong electrolyte)
• Complete or partial speciation of some molecular species.
• Possible salt precipitation and salting-out effect.
• Possible presence of complexing compounds.
• Simultaneous phase and solution equilibrium.
• Mean activity coefficient of a salt completely dissolved.
CA C (sol.)
- ln
c a
ca c c a a
oca ca
A
RT m
1/
1/
where
c a
c a
c a
c a
c a
m m m
• eNRTL model (Chen, 1980)
was used to estimate
Single strong electrolyte solutions
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
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* *, *, *,ln ln ln lnPDH Born lci i i i
2 2*, 21 1
ln 102
Born e ii
s w i
Q zkT r
1/2 2 2 1/2 3/2
*, 1/21/2
2 2 21000ln ln 1
1PDH i i x x
i xs x
z z I IA I
M I
1/21/2 2213 1000
A s e
s
N d QA
kT
21
2x i ii
I x z
, i c a
• Long-range interaction contribution
eNRTL model
• Born term correction (only in mixed-solvent solutions)
* denotes unsymmetric reference state: 1wx Detailed model derivation in Chen and Song (2004).
, i c a
Figure 24. Molecule and ions clusters.
c
a
a
a
c
c
c
m
a
gac gmc
gma
gca
gcm
gmm
gam
eNRTLCation incentre
Anionin centre
Moleculein centre
m
m
m
mm
m
m
m
m
• eNRTL accounts contributions of local and electrostatic interactions.
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
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, ,
,
, ' , '' , '
, '' , ' , '
1ln
k kc ac kc ac k km kmlc m cmk kc a cm
a mc k kc ac k km k kmk k k
k ka c a ka c ac a ca c a k
ca c aa c k ka c a k ka c a
k k
X G X GX G
Yz X G X G X G
X GY X G
X G X G
' ' , ,,m' '
' ,' ' ' , ,
, ,,
,, ,
lnj jm jm k km km k kc ac kc ac
jlc a c mc acmm k km mm mc ac
m c ak km k km k km k kc ac k kc ack k k k k
k ka ca ka cac a Ba ca k
mc cak ka ca k ka ca
k k
X G X G X GY X GX G
X G X G X G X G X G
X GY X G
X G X G
a c
• Short-range interaction contribution
,ln lcm wm mw mwG ,
,
1ln lc
c a wc ac cw cwac
Y Gz
,,ca
1ln lc
a c wa aw awca
Y Gz
*,ln ln lnlc lci i i
, , , , i j k m c a
eNRTL model
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
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, ,
,
, ' , '' , '
, '' , ' , '
1ln
k ka ca ka ca k km kmlc m amk ka c am
c ma k ka ca k km k kmk k k
k kc a c kc a ca c ac a c k
ac a cc a k kc a c k kc a c
k k
X G X GX G
Yz X G X G X G
X GY X G
X G X G
,mcm a caa
G Y G ,mam a aca
G Y G
''
cc
cc
XY
Xa'
'
aa
a
XY
X
,mc cm a m caa
Y ,ma am c m cac
Y
, , ,ma ca am ca m m ca
,ac , ,mc cm ca m m ca
• Adjustable parameters:
• Molecule – molecule
• Ion-pair – molecule
• Ion-pair – ion-pair
' ' ' ', , mm m m mm m m
, , , ,, , ca m m ca m ca ca m
, ' ',ca , ' ' ,
, ' ', , ' ' ,
, , , ,
, ca ca ca ca c a c a ca
ca ca ca ca ca c a c a ca
In practice, α valuesare fixed to 0.2 or 0.3.
• Mixing rules:
eNRTL model
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
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• Case studies
Single strong electrolyte solutions
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
• Used to test eNRTL implementation in GAMS .
• eNRTL model was regressed to experimental data of mean activity coefficient from NaCl and KCl aqueous solutions.
• eNRTL parameters regression problem formulation:
NLP3
2exp modmin = ( )
. . e ( ) 0
1,2,... ; 1,2,...
tn
kzj
L Uij ij ij c c
s t NRTL
i n j n
• Parameter ca,m = 0.2. ,
and ,
are adjusted.
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Table 3. Results of NRTL parameter regression for NaCl and KCl aqueous solutions.
Single strong electrolyte solutions• Results for case studies NaCl and KCl aqueous solutions
MODELLING & PARAMETER ESTIMATION OF LLE AND VLE SYSTEMS
NaCl KCl
, , , ,
GAMS -4.572 8.949 -4.132 8.126
ASPENTECH DB -4.550 8.888 -4.131 8.122
Zemaitis Jr., (1986) -4.549 8.885 -4.107 8.064
Figure 25. Experimental and predicted mean activity coefficient versus molality for NaCl aqueous solution.
Figure 26. Experimental and predicted mean activity coefficient versus molality for KCl aqueous solution.
• AAD%( NaCl) ~ 0.007.
• AAD%(KCl) ~ 0.001.
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SODIUM METHYLATE PRODUCTION PROCESS.
SIMULATION AND ANALYSIS
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Sodium methylate production process
• Traditional production process (Tse, 1997) simply consists upon mixing of Na(s) with
MeOH.
SODIUM METHYLATE PRODUCTION PROCESS. SIMULATION AND ANALYSIS.
• High cost of Na(s) limits the selling price of sodium methylate.
• Alternative process based on RD (Guth, 2004) uses more cheap 50% NaOH (aq.) as raw
material.
• Both these processes are simulated in Aspen Plus and their preliminar economical potentials
estimated.
• Base of production considered for NaOCH3 is 3000 ton per year (dry basis).
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Figure 27. Process for the production of methanolicsolution of sodium methoxide from metallic sodium.
H2 (g)
Na(s)
D=1.31mH=2.19mT ~ 80 ºC
H-601R-601
F-601
25% NaOCH3 inmethanol
CH3OH (g)recycle
Methanolmake-up
SODIUM METHYLATE PRODUCTION PROCESS. SIMULATION AND ANALYSIS.
3 3 2
0 -1rx
1Na+CH OH NaOCH + H
2( H 200.96 kJ mol , Chandran et al. (2007))
Sodium methylate production process
• Traditional production process (Tse, 1997)
1508 kg/h
• Hydrogen is produced as by product.
• Reaction highly exothermic.
1355 kg/h MeOH
160 kg/h
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Sodium methylate production process• Alternative process based on RD (Guth, 2004)
+ -2 3
+ -(aq.) (aq.) (aq.)
+3(MeOH) (MeOH) 3(MeOH)
2H O H O + OH
NaOH Na + OH
NaOCH Na +OCH
• Solution reactions
3 3 2
0 1rx
CH OH+NaOH NaOCH +H O
( H 58.3 kJ mol , Chandran et al. (2007))
• Chemical equilibria
14.41, 7012 K (estimation)A B ln x
BK A
T
• Missing parameters of eNRTL model , 3, 3,
were estimated using methanol activity data in solution with NaOCH3 of Freeguard (1965).
SODIUM METHYLATE PRODUCTION PROCESS. SIMULATION AND ANALYSIS.
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Sodium methylate production process• Alternative process based on RD (Guth, 2004)
•, 3, 3,
estimation:
NLP4 2exp *
, , , ,min = ( ( ) ( ))x
. . e ( , ) 0
tn
i MeOH i MeOH i MeOH i MeOHzi
L Uij ij ij
a
s t NRTL x
SODIUM METHYLATE PRODUCTION PROCESS. SIMULATION AND ANALYSIS.
Figure 28. Experimental and predicted metanol activity versus molality of sodium methylate.
,% ~ 0.002.i MeOHAAD a
, 3
3,
3, , 3
1.180
2.856
0.2
MeOH NaOCH
NaOCH MeOH
NaOCH MeOH MeOH NaOCH
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41/53 SODIUM METHYLATE PRODUCTION PROCESS. SIMULATION AND ANALYSIS.
Figure 29. Process for the production of methanolic solution of sodium methoxide from sodium hydroxide.
H = 14 mT ~ 71 ºCP = 1 bar
50% wtNaOH (aq.)
T-602
H = 28 mT ~ [65;100] ºCP = 1 bar
T-601H2O< 0.1 % wt methanol
30% wt NaOCH3in methanol
CH3OHrecycle
TK-601
CH3OH (g)
Methanolmake-up
H-601
H-602
H-603
R-601
R-602
C-601
Sodium methylate production process• Alternative process based on RD (Guth, 2004)
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Alternative
Fixed capital Waste treatment
Utilities Raw materials
Tradicional
Fixed capital Waste treatment
Utilities Raw materials
Total Costs : 4.7 M€·yr-1
Revenue : 7 M€·yr-1
Economical Potential: 2.3 M€·yr-1
Total Costs : 6 M€·yr-1
Revenue : 6.8 M€·yr-1
Economical Potential: 863 k€·yr-1
SODIUM METHYLATE PRODUCTION PROCESS. SIMULATION AND ANALYSIS.
Sodium methylate production process• Summary
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OPTIMAL DESIGN OF S-L EXTRACTION UNITS
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44/53 OPTIMAL DESIGN OF S-L EXTRACTION UNITS
Figure 31. Rotocel extractor.Figure 30. Crown Model extractor.
Figure 32. DeSmet extractor.
• Can extract large mass flows of oil (2000 ton/day).
• Counter-current cross flow patterns.
• All share the same flow pattern in the extraction
area.
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45/53 OPTIMAL DESIGN OF S-L EXTRACTION UNITS
Mathematical model of a DeSmet extractor
Figure 33. DeSmet extraction area scheme.
2 2
2 2
(1 )= ( )b
m f p p hb
C C C C CV Es K a C C u
z x z x
• Bulk phase equation is
( )=
(1 )p f p p p
vp p d
C K a C C Cu
E x
• Pore phase equation is
• Diffusion and mass transfer with spatial distribution of concentrations in the extraction section are incorporated.
( , , ) ( )=
kmb m s TXm n
b
XHV C x L dx C QdC
d V
• Conservation balance in each tray volume
• The section dimensions, components velocities, and porous media porosities are accounted.
= oil concentration = flakes bed thickness [m] = horizontal coordinate [m] = vertical coordinate [m]
CHxz
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Figure 34. Loading sectionscheme.
Figure 35. Particles filling scheme.
OPTIMAL DESIGN OF S-L EXTRACTION UNITS
2
1= (1 )
1
inp
p s h b b p
CQ HL u u
C
• Average exit concentration is determined by the equation:
uC
1
01
1= ( , , )
X
u rC C x L dxX
2
2
2
2
1=
(1 )1
ps
inp
p vp d p
CC
CCC
EC
Mathematical model of a DeSmet extractor• The flow into the loading zone is determined by the equation:
( )PQ
• Pore phase concentraction in the loading zone:
1
1 1
where, 0,..., ; if 1 and
= ( ( 2) ), ,( ( 1) )
if = 2, ,( 1)s s
s
x X m
x X m X X m X
m m
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47/53 OPTIMAL DESIGN OF S-L EXTRACTION UNITS
Figure 36. Drainage section scheme.
0( , , ) ( , , ) ( )
=
Lskmb m s h f TXms n
b
XH V C x L dx u C X Z dz C Q
dCd V
= =T q D q s h bQ Q Q Q HL u
0( ) = (1 ) (1 ) ( , , )
Lsvf b p p d p fQ Hu E C X Z dz
2(0, , ) = ( ) = 0, , ; > 0sC z C z L from sections =1,...,( 1) :sm m 1( ,0, ) = ( ) > 0mC x C
• Miscella vertical flow rate:
Mathematical model of a DeSmet extractor• Average concentration in the last tray:
• Volume of oil losses:
• Initial & Boundary conditions
( , , ) / = 0 = 0, , ; > 0f sC X z x z L for the drainage zone:
for section ms : ( ,0, ) = = ( ), ,in f ms fC x C x X X X ( , , ) / = 0 = 0, , ; > 0s fC x L z x X bottom boundary:
(0, , ) = ( ) = 0, , ; > 0p pin sC z C z L
0 0( , ,0) = ( , ) and ( , ,0) = ( , )p pC x z C x z C x z C x z
loading zone: Initial values:
= 0, , fx X = 0, , sz L
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48/53 OPTIMAL DESIGN OF S-L EXTRACTION UNITS
Optimal design of a S-L extraction unit
Xs / m X1 / m Ls / m H / m Xms / m ms u / (m/s)
2.0 1.4 2.0 2.4 1.4 6 0.005
Mn / (kg/s) Qq (dm3/s) Cinhe / % Nt / % uh / (m/s) gfe / % ap / (1/m)
9.3 8.8 0.1 21.3 0.002 0.65 72
ρol / (kg/m3) ρhe / (kg/m3)ρMn /
(kg/m3)ρs / (kg/m3) μ / (Pa s) εb εp
910 680 520 1180 3.2E-4 0.4 0.24
• Experimental data fom an industrial DeSmet extractor unit was retrieved
from Veloso (2003).
• PDE system was implemented in GAMS in a discretized form.
• Model was validated against experimental data at S.S.
Table 4. Extrator parameters.
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
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, , ,= min
s.t S.S. model eqs. (discretized FE)
,
,
V H L umop cap
s d L Uu
L U L Um m m
L U
Z C C
C C L L L
u u u V V V
H H H
Optimal design of a S-L extraction unit • NLP for operating and capital cost minimization.
• Capital costs ( Ccap ) ∞ L∙ H
• Operating costs ( Cop ) ∞ QT and power for pumps.
OPTIMAL DESIGN OF S-L EXTRACTION UNITS
• CONOPT solver was used.
NLP5
INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
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Figure 37. Steady state bulk concentration in De Smet extractor. Figure 37. Average concentration in De Smet extractor.
Total costs
Results
OPTIMAL DESIGN OF S-L EXTRACTION UNITS
Parameter Reference Optimal Vm / (m/h) 36 37.54
H /m 2.0 1.946 L / m 10.8 6.943
u / (m/h) 72 54Z / (€/day ) 319.421 224.150
Oil conc. miscella
30% 20%
Table 5. Numerical results summary.
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END NOTES
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Some Future Work
END NOTES
• Process simulation of the whole soy bean-based biorefinery.
• Perform sensibility analysis of the whole process.
• Identify key variables and bottlenecks.
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INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOSÉ F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013
Thank youfor your
attention!
Fundação para a Ciência e TecnologiaMinistério da Ciência, Tecnologia e Ensino Superior
Ph.D grant SFRH/BD/64338/2009
Acknowledgements:Nuno M.C. OliveiraJoão A.P. CoutinhoBelmiro P.D. Duarte