biofuel production processes based on systematic optimization

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)


MOTIVATIONS


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.


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.


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


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).


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


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.


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.


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


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)


PROJECT OVERVIEW

15/53


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.


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)


MODELLING & PARAMETER ESTIMATION OF LLE AND VLE

SYSTEMS


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


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).


20/53

Figure 13. Local molecular clusters.


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



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:



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.



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


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


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.


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


• 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



26/53

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


Study of water – ethanol – IL ternary systems• Results


27/53

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.


• Results


• Concentrations of up to 65% wt in ethanol can

be achieved from 2% wt ethanol feed, using a

single LL extraction stage.


28/53

• 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


• Number of local optima varied between 5 and 77 for the systems [NTf2]- and [Br]-, respectively.


• F(y) varied between -1×10-10 and 0. Therefore all data points were considered stable for the best NRTL parameter set found.


29/53

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



30/53

• 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



31/53

* *, *, *,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.



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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



33/53

, ,

,

, ' , '' , '

, '' , ' , '

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



34/53

• Case studies

Single strong electrolyte solutions


• 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.


35/53

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


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


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).



40/53


•, 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


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


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



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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• Summary

(43/53)


OPTIMAL DESIGN OF S-L EXTRACTION UNITS


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.



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.


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



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



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.


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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.


• CONOPT solver was used.

NLP5


50/53

Figure 37. Steady state bulk concentration in De Smet extractor. Figure 37. Average concentration in De Smet extractor.

Total costs

Results


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


52/53

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.

(53/53)


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

Documents

biofuel production processes based on systematic optimization