jose granjo set2013
TRANSCRIPT
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BIOFUEL PRODUCTIONPROCESSES BASED ON
SYSTEMATIC OPTIMIZATION
METHODOLOGIES
September 18th, 2013
Coimbra, Portugal
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Jos F.O. [email protected]
GEPSI PSE Group, CIEPQPF
Chemical Engineering DepartmentUniversity of Coimbra, Portugal
Nuno M.C. Oliveira
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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
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MOTIVATIONS
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Economy based on fossil resources
MOTIVATIONS
RAW MAT E RIAL S INT E RMEDIATES PRODUCTS/
U S E SC O M M O D I TI E S
S E C O N D A R Y
COMM 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.
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6/53 MOTIVATIONS
RAW MATERIALS
Petroleum
Natural gas
Coal
Tar sa nds bit umin ous
Oil shales
COMMODITIES
Benzene
Gasoline
Diesel
Xylene
Toluen e
Butanes
Ethane/Ethylene
Chlorine
CO / H2
O2/N2
SO2
SECONDARYCOMMODITIES
Ethylene benzene
Cyclohexane
Cumene
P-Xylene
Iso-butylene
Butadiene
Ethylene oxide
Propylene
Ethylene Dichloride
Methanol
Ammonia
Sulphuric acid
Styrene
Adipic acid
Caprolactam
Phenol
Acetone
Tereph tha lic 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, B isphenol A,Caprolactam, Dalicylic acid
Methyl methacrylate, Solvents, Bisphenol A,Pharmaceuticals
Toluen e di isoc yana te, foam poly ure than es
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
TEXTI LS
coatings, foam cushions, upholstery,
drapes, lycra, spandex
SAFE FOOD SUPPLY
Food packaging, preservatives,
fertilizers, pesticides, beveragebottles, appliances, beverage can
coatings, vitamins
TRANS PORTATION
Fuels, oxygenates, anti-freeze, wiper
belts hoses, bumpers, corrosion
inhibitors
CONSTRUCTION
Paints, resins, siding, insulation,
retardents, adhesives, carpeting
RECREATION
Footgear, protective equipment,
tires, wet suits, tapes- CDs-DVDs,
golf equipment, camping gear,
Rboats
COMMUNICATION
Molded plastics, computer casings,
displays, pens, pencils, inks, dyes,
paper products
HEALTH & HYGIENE
Plastics eyeglasses, cosmetics,
detergents, pharmaceuticals, suntan
lotions, medical- dental products,
disinfectants, aspirin
Figure 2. A product flow-chart from petroleum feedstocks.
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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
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8/53 MOTIVATIONS
0
50
100
150
200
250
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Priceindexes(index2005
=
100)
Year
Non-fuel
Industrial
inputs
Fuel
Food
OPEC production
Asian l crisis
9/11
Iraq war
PDVSA strike
Weaker dollar
ArabSpring
Subprime
mortgage crisis
Low spare
production
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
Coal
Gas
Oil
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).
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9/53 MOTIVATIONS
Figure 6. Bio-economy concept.
Bio-economy
B I O MA S S
BIOREFINERIES
BIO-ENERGY
PRODUCT, FUEL AND ENERGY MARKETS
BIO-ECONOMY
BIO-PRODUCTS BIO-FUELS
Market valorises bio-products.
Global market value for bio-productsincreased from 2001 to 2012 from $20 billion to$200+ billion of dollars.
Biofuels global market was $83+ billion in2011 and is forecasted $185 billion of dollars for
2021.
BI OMAS S P R ECUR SOR S SEC ONDARY
C H EM IC ALS I N T E R M E D I A T E S
PRODUCT S
U S E S
INTER MEDIAT E
P LAT F ORM S
BUILDIN G
B L O C K S
Develop bio-products mimicking
functionalities of petroleum-based or improved.
Valorise biomass regarded as waste.
Figure 7. Bio-economy concept.
DEPLOYMENTBIOREFINERYHARVESTING
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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.
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* Scope of this work.
*
MOTIVATIONS
Figure 9. Decisions hierarchy in PSE (Grossmann, 2010).
More focus on process synthesis & analysis.
Decision-making with PSE tools
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Figure 10. Academic & Industry perspectives
(adapted from Neves, 2007).Major concerns in biofuel industry at single-site level are feedstockcosts, 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
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PROJECT
OVERVIEW
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PROJECT OVERVIEW
TR AN SP OR T& HAR V E S TIN G STOR AGE SUR GE B IN
& SCALED ES TONI NG DR YI NG
CRACKING,ASPIRATION& DEHULLING
HULLS &MILL FEED
CONDITIONINGFLAKI NG MILL
SOLVENT
SOLVENTMAR C
F L AS HDESOLVEN-
TI ZI NG
BAG AS S E
EXTRACTION
SOLVENTMAKE U P
YE AS T
ENZYMES
SOLVENTMAKE U P
FLASH
EVAPORATORF L AS H
YE AS TRECYCLE
GLYCEROL
PROTEINCONCENTRATE
BIOETHANOL
WATER
WATER
2SEEDSPREPARATION
1COLLECTING& TRANSPORTING
3SOLVENTEXTRACTION
4BIOETHANOLPROCESS
5BIODIESELPROCESS
MISCELLA
VEG. OIL
SACCHARIFICATION
METHANOL
LLEXTRACTIONREACTION
FERMENTATION
FLOUR
WORTFERMENTEDWORT
SEPARATION
OIL RECYCLE
BIODIESEL
SEPARATION
SEPARATION
WATER+ GLYCEROL
BIODIESEL+ OIL
FIBERS
SOY BEANS
Biorefinery based on whole-crop biomassFigure 11. Whole-crop biorefinery
based upon soy bean.
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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.
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MODELLING & PARAMETER
ESTIMATION OF LLE AND VLESYSTEMS
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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
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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
O
F
F
F
S
O
O
F
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 inNeves et al. (2011).
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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 1
in centre
Molecule 2
in centre
g21
g11
g12
g22
NRTL
1, 2, ...,I IIi i i N
= 1, 2, ...,
I II
i i
I II
i 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 Lg xRT M
lncn
j ij jii ij
ji j j
x G LL
M M M
cn
i k ki ki
k
L x G
cn
i k ki
k
M x G
i j i j
ijG e
( ) , and 0,ij i i ij
ij ij g g g i j i j
RT RT
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Study of water ethanol IL ternary systems
NRTL parameters regression
3 adjustable parameters per binary pair , , .ij j i 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 ijk z
i j k
ijk t
j
L U
ij 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:
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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 abinary mixture system. LLE example.
After regression, stability tests must be done to avoid meaningless parameter values.
00 1.0
m G
R T
X1
X1
L1X1
L2
1L1
1L2
00 1.0
m G
R T
X1
X1
L1X1
L3X1
L2
F(y)
1L1
1L3
1L2
Figure 15. Excess Gibbs energy curve of abinary mixture system. 3 phase LLE example.
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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 iy
i
i
i
i c
F y y y
s t NRTL
y
y i n
Phases are stable if and only if 0
for all space of candidate phase with compositiony.
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
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Figure 17.Ternary phase diagram [TDTHP][Deca] + EtOH + H2Oat 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 + H2Oat 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 quantumcalculations coupled with statistical thermodynamic approaches.
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Figure 19.Ternary phase diagram [TDTHP][CH3SO3] + EtOH + H2Oat 298 K (mass fraction units).
Figure 18.Ternary phase diagram [TDTHP][Cl] + EtOH + H2Oat 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
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Figure 21.Ternary phase diagram [TDTHP][N(CN)2] + EtOH + H2Oat 298 K (mass fraction units).
All systems are type I.
Figure 20.Ternary phase diagram [TDTHP][Br] + EtOH + H2Oat 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 + H2Oat 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 EtOH
extraction (%)
[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 systemat 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 at the optimum varied
between 0.710-3
and 710-3
for systemsBr- and [N(CN)2]- , respectively. Ionic liquid
NRTL binary interaction parameters
13 31 23 32
[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 eachsystem 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 -110-10 and 0.
Therefore all data points were
considered stable for the best NRTL
parameter set found.
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Pervaporation
VaporizerCooling
LL
extractor
Fermenter
Feed
Water
makeup
Broth
Recycle
Residue Solvent
Purge IL
makeup
Extract
Hydrated
ethanol
Anhydrous
ethanol
Water
residue
Figure 23. Block diagram for ethanol purificationbased 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
o
ca 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 lc i i i i
2 2*, 21 1ln 10
2
Born e ii
s w i
Q z
kT r
1/2
2 2 1/2 3/2*, 1/2
1/2
2 2 21000ln ln 1
1
PDH i i x x i x
s x
z z I IA I
M I
1/21/2 221
3 1000A s e
s
N d QA
kT
21
2x i i
i
I x z
,i c a
Long-range interaction contribution
eNRTL model
Born term correction (only in mixed-solvent solutions)
* denotes unsymmetricreference state: 1wx Detailed model derivation inChen 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 in
centre
Anion
in 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 km
k k k
k ka c a ka c a
c a ca c a kca c a
a c k ka c a k ka c a
k k
X G X GX GY
z 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 ac mm k k m mm mc ac
m c ak km k km k km k kc ac k kc ac
k k k k k
k ka ca ka cac a Ba ca k
mc ca
k 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
lc
m wm mw mw G
,
,
1
ln
lc
c a wc ac cw cw ac Y Gz
,
,ca
1ln lc
a c wa aw aw ca
Y Gz
*,ln ln lnlc lc i 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 km
k k k
k kc a c kc a c a c ac a c k
ac a c
c 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 ca
a
G Y G ,mam a ac a
G Y G
'
'
cc
c
c
XY
X
a'
'
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, 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 activitycoefficient from NaCl and KCl aqueous solutions.
eNRTL parameters regression problem formulation:
NLP3
2
exp modmin = ( )
. . e ( ) 0
1,2, ... ; 1,2, ...
tn
kz
j
L U
ij 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 aqueoussolutions.
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 activitycoefficient versus molality for NaCl aqueous solution.
Figure 26. Experimental and predicted mean activitycoefficient 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=1.31m
H=2.19m
T ~ 80 C
H-601R-601
F-601
25% NaOCH3in
methanol
CH3OH (g)
recycle
Methanol
make-up
SODIUM METHYLATE PRODUCTION PROCESS. SIMULATION AND ANALYSIS.
3 3 2
0 -1
rx
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 + OHNaOH Na + OH
NaOCH Na +OCH
Solution reactions
3 3 2
0 1
rx
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 xB
K AT
Missing parameters of eNRTL model ,3, 3,
were
estimated using methanol activity data in solution with NaOCH3 ofFreeguard (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 2
exp *, , , ,min = ( ( ) ( ))x
. . e ( , ) 0
tn
i MeOH i MeOH i MeOH i MeOH z
i
L U
ij ij ij
a
s t NRTL x
SODIUM METHYLATE PRODUCTION PROCESS. SIMULATION AND ANALYSIS.
Figure 28. Experimental and predicted metanolactivity versus molality of sodium methylate.
,% ~ 0.002.i MeOHAAD a
, 3
3,
3, , 3
1.1802.856
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 m
T ~ 71 CP = 1 bar
50% wt
NaOH (aq.)
T-602
H = 28 m
T ~ [65;100] C
P = 1 bar
T-601
H2O
< 0.1 % wt methanol
30% wt NaOCH3
in methanol
CH3OH
recycle
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 Myr-1
Revenue : 7 Myr-1
Economical Potential: 2.3 Myr-1
Total Costs : 6 Myr-1
Revenue : 6.8 Myr-1
Economical Potential: 863 kyr-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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Figure 31. Rotocel extractor.Figure 30. Crown Model extractor.
Figure 32. DeSmetextractor.
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 DeSmetextractor
Figure 33. DeSmetextraction area scheme.
2 2
2 2
(1 )= ( )bm f p p h
b
C C C C C V Es K a C C u
z x z x
Bulk phase equation is
( )=
(1 )
p f p p p
v
p p d
C K a C C C u
E x
Pore phase equation is
Diffusion and mass transfer with
spatial distribution of concentrations in
the extraction section are incorporated.
( , , ) ( )=
kmb m s T
Xm n
b
XHV C x L dx C Q
dC
d V
Conservation balance in each tray volume
The section dimensions, componentsvelocities, and porous media porositiesare accounted.
= oil concentration
= flakes bed thickness [m]
= horizontal coordinate [m]
= vertical coordinate [m]
C
H
x
z
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Figure 34. Loading section
scheme.
Figure 35. Particles filling scheme.
OPTIMAL DESIGN OF S L EXTRACTION UNITS
2
1= (1 )
1
in
p
p s h b b p
CQ HL u u
C
Average exit concentration isdetermined by the equation:
uC
1
01
1= ( , , )
X
u rC C x L dx
X
2
2
2
2
1=
(1 )1
p
s
inp
p v
p d p
CC
CCC
EC
Mathematical model of a DeSmetextractor The flow into the loading zone isdetermined by the equation:
( )PQ
Pore phase concentraction in the loadingzone:
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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/ OPTIMAL DESIGN OF S L EXTRACTION UNITS
Figure 36. Drainage section scheme.
0( , , ) ( , , ) ( )=
Lskmb m s h f T
Xms n
b
XH V C x L dx u C X Z dz C Q
dC
d V
= =T q D q s h bQ Q Q Q HL u
0( ) = (1 ) (1 ) ( , , )Lsv
f b p p d p f Q 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 DeSmetextractor 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 f C x C x X X X
( , , ) / = 0 = 0, , ; > 0s fC x L z x X bottom boundary:
(0, , ) = ( ) = 0, , ; > 0p p
in 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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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) Cin
he / % Nt / % uh / (m/s) gfe / % ap / (1/m)
9.3 8.8 0.1 21.3 0.002 0.65 72
ol / (kg/m3) he / (kg/m
3)Mn /
(kg/m3)s / (kg/m
3) / (Pa s) b p
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.
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, , ,= mins.t S.S. model eqs. (discretized FE)
,
,
V H L um op cap
s d L U
u
L U L U
m 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.
CONOPT solver was used.
NLP5
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Figure 37. Steady state bulk concentration in De Smetextractor. Figure 37. Average concentration in De Smetextractor.
Total costs
ResultsParameter Reference OptimalVm / (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
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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Thank you
for yourattention!
Fundao para a Cincia e Tecnologia
Ministrio da Cincia, Tecnologia e Ensino Superior
Ph.D grant SFRH/BD/64338/2009
Acknowledgements:
Nuno M.C. Oliveira
Joo A.P. Coutinho
Belmiro P.D. Duarte