jose granjo set2013

8/12/2019 Jose Granjo Set2013

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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

[email protected]


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INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOS 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


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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.


6/53INTEGRATED BIOFUEL PRODUCTION PROCESSES BASED ON SYSTEMATIC OPTIMIZATION METHODOLOGIES / JOS F.O. GRANJO / NUNO M.C. OLIVEIRA / UC / SEPTEMBER 2013

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.



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




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.


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

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.


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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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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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


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


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



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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


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.


Results


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


Number of local optima varied

between 5 and 77 for the systems[NTf2]

- and [Br]-, respectively.


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



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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



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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.



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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



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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



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Case studies

Single strong electrolyte solutions


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


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

37/53 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


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).




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,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


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




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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 Summary


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OPTIMAL DESIGN OF S-L

EXTRACTION UNITS

44/53 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

52/53 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

jose granjo set2013

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