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Process simulation and techno-economic analysis of the production of sodium methoxide José Granjo, and Nuno Oliveira Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.5b02022 • Publication Date (Web): 09 Dec 2015 Downloaded from http://pubs.acs.org on December 13, 2015

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Industrial & Engineering Chemistry Research

Process simulation and techno-economic analysis of the production of sodium methoxide José F.O. Granjo∗ and Nuno M.C. Oliveira CIEPQPF, Department of Chemical Engineering, University of Coimbra, Rua Sílvio Lima – Pólo II, 3030–790 Coimbra, Portugal E-mail: [email protected] Phone: +351 239 798 793. Fax: +351 239 798 703

Abstract

1

2

The worldwide increase of biodiesel production has created a major market for

3

sodium methoxide, acting as a catalyst in the transesterification of vegetable oils.

4

This work evaluates different methods of sodium methoxide manufacture, diluted in

5

methanol. Alternative process designs are reviewed and the most promising solutions

6

are modeled in ASPEN Plus® , including their phase and chemical equilibria. Economic

7

indicators are incorporated in a comparative profitability analysis, which includes a risk

8

evaluation with Monte-Carlo simulations.

9

Results show that the manufacturing of sodium methoxide from sodium metallic

10

(Process II) and from sodium hydroxide (Process III) are the most competitive alter-

11

natives, although each can be preferred in different contexts. Process III has a better

12

chance of profitability (41 %) than Process II (34 %), where the latter can be affected by ∗

To whom correspondence should be addressed

1

ACS Paragon Plus Environment


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1

higher raw material costs and the first is more sensitive to the variations in the utilities

2

and capital costs.

3

1

4

Sodium methoxide (NaOCH3) is a basic chemical that has various uses in industry. Its

5

base strength makes it ideal as a catalyst, condensation and reduction agent in organic

6

synthesis 1 . It is an intermediate in flavor and fragrance synthesis and a starting material for

7

additives in adhesives and paints. Its applications also span to agrochemicals, manufacturing

8

of pharmaceuticals (e.g., vitamins and ibuprofen), and as a heat transfer fluid in nuclear

9

reactors. More recently, NaOCH3 diluted to 25 to 30 % in methanol has been increasingly

10

Introduction

employed as a transesterification catalyst in biodiesel production.

11

In general, the synthesis method of any metal/metalloid alkoxide depends on the elec-

12

tronegativity of the metal and on the chemical character of the alcohol. Highly electropositive

13

metals react readily with alcohols forming the corresponding metal alkoxides and releasing

14

hydrogen, while less electropositive metals (e.g., Mg and Al) require a catalyst (I2, HCl,

15

or HgCl2) for a more complete reaction. Alkoxides are also produced from metal covalent

16

halides, and their corresponding hydroxides, oxides, or amides when reacted with the alco-

17

hol 2 . For higher periodic table metals, alkoxides may also be obtained from electrochemical

18

synthesis where anodic dissolution of metals/metalloids occur in the presence of a conduct-

19

ing electrolyte 2 . Additionally, chemical vapor deposition 3 and metal atom vapor techniques 2

20

may also be selected for the production of alkoxides with more specific uses.

21

In particular, for the synthesis of NaOCH3, four distinctive methods are described in the

22

literature, which may be lumped in terms of the raw materials employed: sodium amalgam

23

(Process I), sodium metal (Process II), sodium hydroxide (Process III), and sodium acetate

24

(Process IV). In Process I, NaOCH3 is produced from a sodium amalgam (NaHg), a by-

25

product of the caustic-chlorine process with mercury-electrode cells. The liquid amalgam

2


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1

enters at the top of a packed column, contacting with the ascending methanol vapor in

2

excess, and stripping the sodium out of the amalgam 4,5 . The high boiling points of Hg and

3

NaOCH3 ease the separation of methanol that can be recycled without further purification

4

steps, since the distillate is virtually pure in methanol. A contacting electrode of graphite

5

or Fe–Cr–C alloy, which cannot be amalgamated, may be used to accelerate the reaction 6,7 .

6

The final concentration of methanol at the bottom of the distillation column is adjusted by

7

controlling the reflux ratio in the condenser. The mercury fraction, sodium-free, is recovered

8

and reused in chlorine and sodium metal production through the Castner–Kellner process 8 ,

9

or in caustic soda manufacture with mercury-electrode cells 9 .

10

A patent of Tse 10 discloses a continuous process for the production of NaOCH3 from

11

sodium metal (Na) and methanol (Process II). Na is melted and introduced in an agitated

12

jacketed vessel, reacting with methanol in a highly exothermic reaction. Therefore, the

13

methanol mass flow rate is controlled so that the temperature inside the reactor remains

14

within 80 to 86 ◦C and the concentration of NaOCH3 does not exceed 28 wt% to prevent

15

sodium build-up and reactor overpressuring. A mass flow rate of methanol 8 to 10 times

16

the mass of molten sodium is recommended, using a reactor filled up to 60 % with reacting

17

liquid. Due to the release of H2 in the reaction, inert gas (N2) is used during the startup and

18

shutdown phases, and the reactor is pressurized to maintain an oxygen-free atmosphere and

19

avoid the danger of explosion. Later patents 11,12 describe the production of sodium alkoxides

20

of higher alcohols (e.g., tert−butanol, tert−amyl) with similar apparatus, where catalysts

21

like FeCl3 are included to accelerate the reaction, as these tend to be slower.

22

In Process III, the methanolic solution of NaOCH3 is produced from caustic soda and

23

methanol in a reactive stripping column 13 . Methanol vapor in excess is introduced at the

24

bottom of the column and contacts the aqueous NaOH fed at top, flowing in countercurrent,

25

driving the methoxylation reaction towards the products. The concentration of NaOCH3 in

26

the product stream is adjusted to be within 20 to 30 wt% in methanol, according to the re-

27

quirements. Vapor distillates exiting the top of the distillation column containing 3 to 10 wt% 3



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1

of water in methanol, are fed to a second distillation to recover the methanol and send the

2

remaining water to waste treatment. Since the presence of water in methanol affects nega-

3

tively the methoxylation reaction conversion, its concentration must be kept very low. Guth

4

et al. 14 disclose a similar process, comprising a reactive distillation and a methanol/water

5

fractionation column. A variant of this design is presented in Shao and Wang 15 , where

6

NaOCH3 is produced from NaOH previously dried, mixed with methanol and fed into a

7

reactive distillation column where it contacts with methanolic vapor in countercurrent.

8

An alternative design based on electrodialysis (Process IV) is proposed by Sridhar 16 to

9

produce NaOCH3 and acetic acid (CH3COOH) in equimolar proportions from NaOOCCH3

10

and methanol. The diluate (11 wt% of NaOOCCH3 in methanol) enters the electrodialysis

11

cells stack and contacts the anode that is under electric load, releasing Na+ ions. These cross

12

a cation exchange membrane and react with methanol, generating NaOCH3 and releasing H+.

13

The H+ ions pass through a bipolar membrane and react with the acetate ions, generating

14

CH3OH. The reaction between the ions Na+ and methanol at the cathode releases H2 and

15

NaOCH3.

16

Process I is the oldest in use by the industry and is very cost-efficient, due to its simplicity

17

and relatively low operating and raw materials costs. It is usually integrated in the chemical

18

supply chain of caustic-chlorine manufactures using mercury-electrode cell units, which still

19

represent one-third of the total chlorine capacity in Europe 9 . However, Process I is expected

20

to be abandoned worldwide due to health and environmental issues concerning the use of

21

mercury and its traces in the product, propagating the contamination into various ecosys-

22

tems. In Europe, all mercury-based caustic-chlorine manufactures are scheduled to change

23

their technology before 2020 9 . Process II is relatively simple, yielding NaOCH3 with high

24

purity. However, the high reactivity of Na, the synthesis of H2, and the high exothermicity

25

of the methoxylation makes this process particularly hazardous, needing to be controlled

26

closely to avoid the risk of explosion. Unlike Process II, Process III has the advantages of

27

requiring cheaper raw materials and being safer to operate, although it requires a higher 4


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1

energy consumption needed to separate methanol from water and to maintain a large flow of

2

recycled methanol. Process IV has the advantage of producing highly concentrated and pure

3

NaOCH3 in methanol. However, this alternative has a high energy consumption (no less than

4

56 GJ t−1 of NaOCH3 in dry mass), needs an extra step to produce the diluate from the acetic

5

acid exiting of the electrodialysis cells stack, and is difficult to scale-up. The production ca-

6

pacity of NaOCH3 reported in Sridhar 16 varies between 43 and 162 g m−2 h−1 , obtained by a

7

membrane with 100 cm2 of effective cross-sectional area. Therefore, Processes I and IV were

8

discarded from a more detailed analysis: the former because it employs a technology that

9

is being abandoned due to its environmental impacts; and the latter because it has not yet

10

reached the commercialization stage. Herein, Process II and III are assessed in more detail,

11

through detailed process modeling based on rigorous estimation of thermophysical properties

12

and complete mass and energy balances.

13

The paper is organized as follows: Section 2 presents the modeling approach used to de-

14

scribe the thermophysical properties of the components and mixtures, the phase and chemical

15

equilibria existing in both processes. Due to the strong non-ideality of the phase behavior

16

of these systems, this task required not only collecting property data from a number of

17

sources, but also involved detailed thermodynamic modeling of the phase equilibrium, using

18

raw experimental data and the application of a nonlinear optimization framework; this is

19

described in Section 2.2. Simulation results for the base conditions and the overall model

20

validation are presented in Section 3. The information gathered from the mass and heat

21

balances, thermophysical properties, stream compositions, together with the operating pa-

22

rameters supported the sizing of equipment units and the identification of utilities and raw

23

materials needs. The economic performance of each alternative is characterized in Section 4,

24

including the break even prices of NaOCH3, the net present value (NPV), present value ratio

25

(PVR), the discounted payback period (DPBP), and the non-discounted rate of return on

26

investment (ROROI) of both processes. The paper concludes with a comparative profitabil-

27

ity analysis of the processes studied and Monte-Carlo simulations to quantify the potential 5



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1

risks associated with uncertainties in the key economic parameters determined.

2

2

3

Giving the character of the mixtures involved, the Non-Random Two-Liquid (NRTL) 17 and

4

electrolyte Non-Random Two-Liquid (eNRTL) 18,19 models were used to, respectively, model

5

the liquid phase of Processes II and III. Since the components contained in the mixtures are

6

of comparable molecular sizes, the nonideal entropy of mixing tends to be negligible when

7

compared with the heat of mixing, which is consistent with the underlying assumption behind

8

the NRTL and eNRTL models. Moreover, the excess heat of mixing term was discarded

9

in the simulations. In Process II, it is assumed that no speciation exists and since H2

10

is a light gas, it is described as a Henry’s component. The Redlich-Kwong equation of

11

state 20 was selected to model the non-ideality of the vapor phase and the coefficients for

12

the Henry’s constants correlation and the NRTL binary interaction parameters (BIPs) of

13

methanol-water were taken from the ASPEN Plus® built-in properties database 21 . The

14

methoxylation reaction is considered to be instantaneous and irreversible, with a heat of

15

− ◦ ) of −203.95 kJ mol−1 : reaction 22 (∆Hrx

Phase and chemical equilibria modeling

NaOCH3 +

Na + CH3OH

1 H2 2

[R 1]

16

The mixture NaOCH3-NaOH-water-methanol in Process III participates in simultaneous

17

chemical and vapor-liquid phase equilibria (VLE), where the speciation in the liquid phase

18

is no longer negligible. The modeling of these equilibria is detailed next.

6


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1

2.1

Chemical equilibrium

2

When sodium is added to a mixture of water and methanol, a hydroxide-methoxide ionic

3

equilibrium is usually instantaneously established 23 : CH3OH + OH–

Khm

CH3O– + H2O

[R 2]

4

In liquid mixtures of methanol and water, both solvents undergo autoprotonation reactions

5

as follows: CH3OH + H2O

2 H 2O

Ka

Kw

H3O+ + CH3O–

[R 3]

H3O+ + OH–

[R 4]

6

The equilibrium constants of the hydroxide-methoxide equilibrium (Khm ), methanol acid

7

dissociation (Ka ), and water autoprotolysis (Kw ) are expressed in terms of activities

Khm =

a(x)OCH3– a(x)H2O , a(x)CH3OH a(x)OH–

Ka =

a(x)OCH3– a(x)H3O+ , a(x)H2O a(x)CH3OH

Kw =

a(x)H3O+ a(x)OH– a(x)2H2O

(1)

where a(x) represent the species activity in a mole fraction scale. These equilibrium con0 stants are interconnected, as Khm = Ka /Kw ; or if one considers diluted solutions: Khm =

Ka0 [H2O]/Kw0 . The symbol

0

stands for the equilibrium constant in a molarity basis and

[H2O] the molar concentration of water (mol dm−3 ). Khm , Ka , Kw may be broken into the product between a regular solution term (Kx ) and non-ideal term (Kγ ) in a mole fraction scale (Eq. (2)), or Kc and KΓ using a molar scale (Eq. (3)). The values of the equilibrium constants in both scales are interrelated; however process simulators do these conversions

7



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internally and therefore further details are omitted here.

Khm =

a(x)OCH3– a(x)H2O xOCH3– xH2O γOCH3– γH2O = = Khm,x · Khm,γ a(x)CH3OH a(x)OH– xCH3OH xOH– γCH3OH γOH–

(2)

0 = Khm

a(c)OCH3– a(c)H2O [CH3O ] [H2O] ΓOCH3– ΓH2O = = Khm,c · Khm,Γ a(c)CH3OH a(c)OH– [CH3OH] [OH ] ΓCH3OH ΓOH–

(3)

The expression for ln Khm,c versus 1/T is obtained from the linear regression (Eq. (4)) of experimental data 23 and the equilibrium constant of water in mole fraction basis (Kw,x ) is given by Eq. (5) 21 .

ln Khm,c = −4.374 + 1751/T

(4)

ln Kw,x = 132.89888 − 13445.9/T − 22.477301 ln T

(5)

Because only two of the three reactions 2, 3, and 4 are independent, Reaction 2 and 4 were modeled in the ASPEN Plus® process simulator. Additionally, Reaction 2 is modified into a similar form (Reaction 5), where a(·)NaOCH3 = a(·)Na+ a(·)OCH3– . CH3OH + Na+ + OH– NaOCH3

NaOCH3 + H2O

Na+ + OCH3–

[R 5] [R 6]

1

2.2

Phase equilibrium

2

The description of the VLE of NaOCH3, NaOH, H2O, and CH3OH system with eNRTL

3

requires, in its full form, 13 pairs of unsymmetrical BIPs:

4

• Molecule-molecule: τCH3OH,H2O

5

• Ion pair-molecule: τNa+OH–,H2O , τNa+OCH3–,H2O , τNa+OH–,CH3OH , τNa+OCH3–,CH3OH ,

6

7

τH3O+OH–,H2O , τH3O+OCH3–,H2O , τH3O+OH–,CH3OH , τH3O+OCH3–,CH3OH • Ion pair – ion pair: τNa+OH–,Na+OCH3– , τH3O+OH–,H3O+CH3O– , τNa+OH–,H3O+OH– , 8


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1


τNa+OCH3–,H3O+OCH3– .

2

We also need to consider the 13 symmetrical non-randomness parameters of the respective bi-

3

naries: αCH3OH,H2O , αNa+OH–,H2O , αNa+OCH3–,H2O , αNa+OH–,CH3OH , αNa+OCH3–,CH3OH , αH3O+OH–,H2O ,

4

αH3O+OCH3–,H2O , αH3O+OH–,CH3OH , αH3O+OCH3–,CH3OH , αNa+OH–,Na+OCH3– , αH3O+OH–,H3O+CH3O– ,

5

αNa+OH–,H3O+OH– , αNa+OCH3–,H3O+OCH3– .

6

In this case, the ion-ion pairs short-range interactions tend to be small compared with

7

the ones of ion pair-molecule and long-range ion-ion electrostatic forces, because the salts

8

are very diluted due to the great excess of methanol inside the column, which also favors the

9

equilibrium described by Reaction 5 towards NaOCH3. It is also assumed that short-range

10

interactions between NaOH and CH3OH may be negligible since NaOH is more strongly

11

solvated in water than in methanol due to the existing hydrogen bonds between OH– and

12

water. Also, the ion OCH3– tends to be more surrounded by methanol molecules than by

13

water inside the distillation column, and therefore its interactions with methanol tend to be

14

more predominant than with water. Finally, it is postulated that the short-range energies

15

of interaction between H3O+ containing electrolytes with water and methanol are very weak

16

when compared to those of the Na+ cation.

17

This greatly reduces the number of eNRTL BIPs needed: τCH3OH,H2O , τNa+OH–,H2O ,

18

τNa+OCH3–,CH3OH , αCH3OH,H2O , αNa+OH–,H2O , αNa+OCH3–,CH3OH . The values of eNRTL BIPs for

19

the pairs CH3OH, H2O and Na+OH–, H2O were retrieved from the ASPEN Plus® database,

20

while the parameters τNa+OCH3–,CH3OH were still lacking. The VLE experimental data for

21

this system are very scarce and therefore the missing eNRTL BIPs were estimated from the

22

activities of methanol (ˆ aCH3OH ) in the NaOCH3 and CH3OH system, reported by Freeguard

23

et al. 24 at 25 ◦C, using tensio-gravimetric analysis. Several approaches have been considered for the parameter estimation problem with the eNRTL model to obtain the best set of BIPs that describe the VLE data of mixtures containing salts 25,26 . In the current work, this task was formulated as the solution of a nonlinear programming problem, using the sum of squares of the differences between the experimen9



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Page 10 of 42

tal activity coefficients of methanol in the symmetric convention (ˆ γCH3OH,i ) and the values predicted by the model (γCH3OH,i ) as the objective function (ϕ) in Eq. (6). As the reported methanol activities are normalized to Raoult’s law and the eNRTL to the infinite dilution ∗ ∞ of salts in an aqueous solution, γCH3OH,i in Eq. (6a) is substituted by γCH + γCH . 3OH,i 3OH,i

The vector of model parameters (θ ≡ {τNa+OCH3–,CH3OH , τCH3OH,Na+OCH3– }), were determined by minimizing ϕ, subjected to the eNRTL model and phase equilibria equations, compactly expressed as eNRTL(·|θ), with lower and upper bounds for θ. Since the experimental data is only available at 25 ◦C, the θ parameters in the model were considered constant with temperature. This formulation was implemented in GAMS 27 and solved with MSNLP 28 , a heuristic and multistart global solver used to handle the non-convexity of the problem, employing the CONOPT solver 29 to perform the local search. Problem Eq. (6) is small (691 equations and 695 variables), but also highly non-linear. This required less than 1 s of CPU time to find a local solution with CONOPT, after extensive algebraic reformulations, with a proper initialization scheme. More details relative to the eNRTL parameter estimation problem are presented in the Supporting Information.

min z

ϕ=

np X

∗ ∞ γˆCH3OH,i − γCH + γCH 3OH,i 3OH,i

2

(6a)

i=1 ∗ ∞ s.t. γCH , γCH = eNRTL(·|θ)i 3OH,i 3OH,i

(6b)

LB ≤ θ ≤ U B

(6c)

3×np γ ∗ , γ ∞ ∈ (R+ , θ ∈ R2 , 0)

i = {1, 2, . . . , np }

(6d)

1

Figure 1 shows that the predictions of the eNRTL model are in good agreement with the

2

experimental data and that γCH3OH in sodium methoxide is almost linearly dependent on its

3

mole fraction within the range of 77 to 85.5 % mol. The average relative deviation (ARD)

4

of the reported methanol activities is 0.41 % and the value of ϕ is 1.0775 × 10−5 , for the

5

best set of BIPs found: τNa+OCH3–,CH3OH = −5.352, τCH3OH,Na+OCH3– = 2.297. These values

6

confirm that the interactions between the cation Na+ and anion OCH3– are stronger than 10


Page 11 of 42

0.60

Methanol activity coefficient (γCH3OH)

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0.55

Data from Freeguard 24 eNRTL

0.50

0.45

0.40

0.78

0.80

xCH3OH

0.82

0.84

Figure 1: Methanol activity coefficient versus mole fraction, in a mixture with sodium methoxide at 25 ◦C. 1

the interactions between methanol and these ionic species; and that the latter is stronger

2

than the interaction between two methanol molecules. This is because τNa+OCH3–,CH3OH has

3

a negative value while τCH3OH,Na+OCH3– is positive, consistent with the physical meaning of

4

τ which represents the difference of interaction energies between chemical species 18 . The

5

boiling point of methanol was also found to elevate rapidly with the addition of NaOCH3,

6

as the methanol activity coefficient γCH3OH decreases from 0.62 at 85.5 % mol to 0.38 at

7

77 % mol. This indicates that strong forces of interaction are established between methanol

8

and NaOCH3 in solution.

9

3

Process simulation and model validation

10

As a basis for this study, the capacity of NaOCH3 production was established by assessing the

11

approximate amount of catalyst needed in a 300 kt/y biodiesel production plant (considering

12

the use of 1.8 wt% per mass of vegetable oil). This capacity represents a typical large-scale

13

biodiesel plant currently operating in Europe 30 . The mass flow rate of NaOCH3 is approxi-

11



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1

mately 675 kg h−1 or 5.40 kt/y in dry mass (DM), and the product is marketed without any

2

intermediate purification stages, with a concentration of 25 to 30 wt% in methanol. In Pro-

3

cess II, the H2 production credits are also accounted, admitting that all the amount produced

4

is also marketed without any further processing. Regarding the raw materials, the concen-

5

tration of NaOH is assumed to be 50 wt% in water with residual components, like sodium

6

chloride or carbonate discarded. Similarly, methanol and sodium metal are taken as anhy-

7

drous and free of trace components. The maximum concentration of water and non-reacted

8

alkali content allowed in the product are 0.1 wt% and 0.4 wt%, respectively.

9

3.1

Process II

10

The reactor R-501 in Figure 2 was simulated with RSTOIC, a stoichiometric reactor model

11

available in the ASPEN Plus® process simulator, based on known fractional conversions. Re-

12

action 1 has a estimated conversion of Na 10 equal to 0.997. A flash separation model (F-501)

13

is artificially added to model the H2 vents in R-501. The nitrogen employed during startup

14

and maintenance stops to create the inert atmosphere inside the reactor was disregarded in

15

the simulations, since no nitrogen is circulating in the reactor during steady-state operation.

16

The mass flow rates reported in Tse 10 were multiplied by 1.77 to obtain the production basis

17

established before for NaOCH3. R-501 was set to operate at 83 ◦C, as suggested elsewhere 10

18

and at 14.3 bar to reduce to 1 % the mass flow of methanol carried away in the H2 stream

19

and prevent the formation of excessive vapor inside the reactor.

20

The heat-exchanger H-501 represents the melting of sodium metal that can be done

21

in a tank car to minimize the contact of sodium with air, using a circulating heating oil

22

through the jacketed coil inside the tank car 10 . Figure 2 contains the flowsheet diagram

23

as simulated in ASPEN Plus® and Table 1 shows the stream summary results. The simu-

24

lations show that the consumption of methanol in the base case conditions corresponds to

25

8.5 kg/kg of sodium metal.

12


Page 12 of 42

Page 13 of 42

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


METHANOL P-501

H2

502

503

H-501

NAOCH3

504

501

NA

R-501

P-502

F-501

Figure 2: Flowsheet of Process II as simulated in ASPEN Plus® .

Table 1: Stream data of Process II. NA

METHANOL

H2

NAOCH3

Temperature (◦C) 30.0 Pressure (bar) 1.01 Vapor fraction 0 −1 Mole flow (kmol h ) 12.54 Mass flow (kg h−1 ) 288.2

30.0 1.01 0 76.10 2438

83.0 14.27 1 6.838 37.89

83.0 14.27 0 75.55 2689

Component Mass Flow (kg h−1 ) CH3OH 0 NaOCH3 0 Na 288.2 H2 0

2438 0 0 0

25.73 0 0 12.17

2012 675.0 0.968 0.427

1 0 0 0

0.679 0 0 0.321

0.748 0.251 360 ppm 159 ppm

Mass fraction CH3OH NaOCH3 Na H2

0 0 1 0

13



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

3.2

Process III

2

Before determining the base case conditions and simulating the flowsheet for the capacity

3

established, the chemical and phase equilibrium models developed in Section 2 were first val-

4

idated with pilot plant data of two similar processes described in Guth et al. 14 and Shao and

5

Wang 15 . The reactive distillation column (here D-501) and the methanol recovery distillation

6

column (here D-502) were simulated with the RadFrac model, which is a rigorous and flexible

7

model to simulate multi-stage vapor-liquid or vapor-liquid-liquid separations (e.g., distilla-

8

tion, extractive, azeotropic, reactive, and absorption columns). The solution reactions (water

9

autoprotolysis and salts dissociation) were introduced in the Reaction Chemistry input form

10

of the ASPEN Plus® process simulator while the methoxylation reaction was described as

11

a Reac-Dist type of reaction in the Reactions input form, and included in RadFrac model of

12

D-501. Since the chemical equilibrium in D-501 is assumed instantaneous, the reaction ex-

13

tension in each stage is solely determined by the chemical and phase equilibria. The number

14

of stages in the reactive zone was considered identical to the number of theoretical stages,

15

since the reactions take place homogeneously in the liquid phase.

16

The input data for both case studies, simulation results and a detailed discussion of

17

these can be found in Section C of Supporting Information. In general, good agreement is

18

observed between the simulation results and the pilot plant data, with typical error predic-

19

tions below 2 % in the mass flow rates and the NaOH conversion values. Some discrepancies

20

are observed in the column temperature profiles reported in Shao and Wang 15 and those

21

obtained in our simulations. However, no definitive conclusions can be drawn, since the data

22

previously reported are relative to interval operating conditions. Moreover, the simulation

23

results of the sensitivity analyzes performed elsewhere 15 and replicated in this work show

24

that our predictions of the NaOH conversion are more conservative and less sensitive to the

25

operating parameters studied. The differences are explained by the Khm,c values reported in

26

Shao and Wang 15 and in Murto 23 , where the latter is used in this work. The temperature

27

dependency of Khm,c , estimated with Eq. (4), suggests that the methoxylation reaction of 14


Page 14 of 42

Page 15 of 42

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1

NaOH in aqueous solution is less exothermic than predicted with the correlation in Shao and

2

Wang 15 (less negative slope of ln Khm,c ), which is also more consistent with the calorimetric

3

data of Chandran et al. 22 . Hence these results indicate that the present model is able to

4

predict reasonable well the system under study and that it can be used for the simulation of

5

Process III. Furthermore, this model framework is also a valuable tool for process design and

6

to assist in locating operating regions of interest where further experimental work should

7

be done to overcome current limitations, including VLE and Khm,c data covering all the

8

temperature range in Process III.

9

3.2.1

Base case simulation

10

The structure considered for Process III is presented in Figure 3, and its base case operating

11

conditions were chosen for the production capacity of NaOCH3 established earlier in this

12

section. Following the reference data described previously 13,14 , the operating pressure for

13

columns D-501 and D-502 was initially set at 1 atm, where the former has no condenser and

14

the latter uses a partial-vapor condenser. The caustic soda is heated and introduced at the

15

top while the saturated methanol vapor is fed at the bottom of D-501 and recycled in D-502;

16

water is eliminated from the process at the bottom of this column. A vapor compressor (C-

17

501) and a pump (P-501) were also added to the process diagram, to provide the required

18

pressure head to the feed streams of D-501.

19

A reference size of 20 theoretical stages was first considered for column D-501, which was

20

then simulated separately from the remaining units (i.e., with no methanol recycle). Using

21

the product specifications — 675 kg h−1 of NaOCH3 in a 30 wt% methanol solution, contain-

22

ing less than 0.4 wt% of NaOH and less than 0.1 wt% of water — this base configuration

23

allowed the calculation of the required input streams and the NaOH conversion in this unit.

24

The estimated conversion value was 0.9823, obtained with a methanol to water mass flow

25

ratio (F5043

26

flow rate would reduce the NaOH conversion, while the use of a more diluted NaOH feed

CH OH

H O

2 /FNAOH,502 ) equal to 30.6. Under these conditions, decreasing the methanol

15



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 42

506

503

METHANOL H-501

D-502 505

C-501

501

NAOH P-502

WASTE

502 504

H-502

D-501

30NAOCH3

Figure 3: Flowsheet of Process III as simulated in ASPEN Plus® . 1

would simultaneously require a higher methanol feed mass flow rate and reboiler duty to keep

2

the product within the specifications and the concentration of NaOCH3 around 30 wt%.

3

The final number of theoretical stages (NT) for D-501 was selected through a sensitivity

4

analysis, by varying NT between 17 and 27 and adjusting the reboiler duty to maintain

5

constant the NaOCH3 product concentration. Two moisture levels in the feed methanol

6

vapor were considered in this analysis: water-free and with 100 ppm, using the bottom/top

7

mass ratio F5043

8

recycled methanol stream decreases the NaOH conversion, while simultaneously increasing

9

its residual concentration in the product stream. This scenario was consequently used to

10

determine the minimum number of theoretical stages required to keep the residual NaOH

11

concentration within specifications. In the range of values considered, we also found that

12

the reboiler duty of D-501 increases only slightly with the NT value (for instance, from 7.03

13

to 7.05 GJ h−1 , when the methanol stream contains 100 ppm of water). This is because for

14

higher NT, the conversion in NaOCH3 rises from 0.981 to 0.984, forming more water that

15

needs to be evaporated to prevent product contamination. Consequently, the residual traces

16

of NaOH drop from 0.43 to 0.37 wt% with NT, in this interval range. Considering both

17

effects simultaneously, it is clear that columns with less equilibrium stages also require less

CH OH

H O

2 /FNAOH,502 defined earlier. We found that the presence of water in the

16


Page 17 of 42

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1

energy to operate, with the compromise of originating larger NaOH residual concentrations

2

(which might exceed the established limits), while columns with higher NT increase the

3

NaOH conversion at the expense of larger investment and slightly larger operating costs,

4

required to remove the extra water formed. Quantitative information from this sensitivity

5

analysis is presented in Section D of the Supporting Information, where the optimal region

6

of NT for the column D-501 is located, leading to the adoption of the final value of NT = 20.

7

Relatively to the D-502 column, the mass reflux ratio (RRmas ) and the reboiler duty were

8

chosen so that the water mass fraction in the distillate is below 100 ppm and the methanol

9

concentration in the bottom stream is below 0.1 %, so that the wastewater can be considered

10

nonhazardous, avoiding more expensive waste treatments. The feed stage location (NF) and

11

the NT for D-502 were first estimated with the distillation column model DSTWU, which

12

uses a shortcut Winn-Underwood-Gilliland method 21 . The first estimate for NT was 20,

13

with NF = 13, and and RRmas = 1.1. The values of NF and RRmas were then tuned with the

14

RadFrac model, in order to meet the specifications for a varying NT. Results show that NT

15

starts to increase sharply for RRmas below 1.2 and minimal savings in terms of reboiler duty

16

are observed for NT and NF above 29 and 23, respectively. Final values for NT and RRmas of

17

D-502 were selected as, respectively, 32 and 1.03, by minimizing the annual operating costs

18

of the process (Section D of the Supporting Information). These operating conditions are

19

similar to the ones provided in example 1 of Guth et al. 14 and are within the range of these

20

operating parameters disclosed in the same patent.

21

The numerical results of the simulations for Process III at the base case conditions are

22

presented in Table 2. The mass flow rate of the methanol make-up (stream METHANOL) is

23

1983 kg h−1 , corresponding to a METHANOL/NAOH mass flow ratio of 1.95. The conversion

24

of NaOH is 0.983, which is lower than the reaction conversion of NaOH in Process II (0.997).

25

The distillate-to-feed mass ratios of D-501 is 0.863 and of D-502 is 0.949.

17



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 42

Table 2: Stream data of Process III. Stream

NAOH METHANOL

504

505

506

WASTE 30NAOCH3

Temperature ( C) Pressure (bar) Vapor fraction Mole flow (kmol h−1 ) Mass flow (kg h−1 )

30.0 1.01 0 53.65 1017

30.0 1.01 0 61.89 1983

158.0 3.04 1 484.7 15531

70.7 1.01 1 463.5 14281

65.0 1.01 1 422.9 13548

100.0 1.01 0 40.67 733.0

95.5 1.01 0 62.38 2267

Component Mass Flow (kg h−1 ) H 2O NaOH NaOCH3 CH3OH

508.5 508.5 0 0

0 0 0 1983

1.287 0 0 15530

733.6 0 0 13547

1.287 0 0 13547

732.3 0 0 0.718

1.241 8.803 674.9 1582

0.5 0.5 0 0

0 0 0 1

83 ppm 0 0 1

0.051 0 0 0.949

95 ppm 0 0 1

0.999 0 0 979 ppm

547 ppm 0.004 0.298 0.698

◦

Mass fraction H 2O NaOH NaOCH3 CH3OH

1

4

2

The capital costs of these process alternatives was determined using the module costing

3

technique 31,32 . This method relates the global cost to the purchase cost of the major equip-

4

ment items, evaluated at base conditions. Various factors are used to incorporate additional

5

direct and indirect costs incurred, in the total module cost (e.g., instrumentation, piping,

6

foundations and structural supports, construction overheads and auxiliary facilities). This

7

analysis may be classified as a study estimate given that only the major investment expenses

8

are explicitly considered and that the main pieces of equipment are roughly sized using em-

9

pirical correlations and heuristics. The uncertainty of these studies are within 33 −25 and

10

40 %; consequently, for an accurate profitability estimation, more detailed equipment and

11

flowsheet specifications would be required.

12

13

14

15

Economic analysis

Capital costs were estimated assuming the construction of a new facility (grassroots design), which can be broken into three contributions 31–33 : P • Total bare module costs ( j CBM,j ), which is the sum of the capital cost of each equipment unit estimated using the module costing technique;

18


Page 19 of 42

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1

2

3

4

5


• Contingency costs, added to cover unexpected expenses resulting of data cost uncerP tainty and flowsheet completeness (CCF = 0.18 j CBM,j ); • Auxiliary facilities costs, which include complementary items to the process (e.g., land purchase, off-sites and utility systems). These are assumed to be 50 % of the total bare P 0 model costs at base case conditions (CAF = 0.5 j CBM,j ).

6

The bare cost of each module (CBM = CP0 FBM ) is typically computed from a correlation

7

of the purchased cost function at base conditions (CP0 ) and factors for the specific materials

8

9

of construction and the operating pressure selected (FBM ). Consequently, the total module P cost is given by CTM = j CBM,j + CCF , while the overall grassroots cost is calculated as

10

CGR = CTM + CAF . The values for CP0 in Turton et al. 33 were updated to December of

11

2014 using the Chemical Engineering Plant Index (CEPCI), which is 575.8 against 397 in

12

September 2001, date of reference 33,34 .

13

The total manufacturing costs account for the hourly operation expenses of a chemical

14

plant and are the sum of the direct manufacturing costs (e.g., chemical materials, utilities,

15

royalties, labor fees, maintenance, repairs), fixed manufacturing costs (e.g. overheads, stor-

16

age, local taxes, insurances, packaging, depreciation), and general expenses (administrative

17

costs, distribution and selling, R&D). The total manufacturing costs excluding depreciation

18

(COMd ) are given by 33 COMd = 0.18FCIL + 2.76COL + 1.23 (CUT + CWT + CRM ), where

19

FCIL is the total depreciable fixed capital investment (here equal to CGR ), COL is the op-

20

erating labor cost, CUT the utilities cost, CWT the cost of waste treatment, and CRM the

21

raw materials cost. The total capital investment is TCI = FCIL + FCIland + FCIwork , where

22

besides FCIL , TCI also includes the land cost (FCIland = $600 K) and the working capital

23

(FCIwork ) estimated from FCIwork = 0.1 (CRM + FCIL + COL ). The FCIland value is based

24

on an estimate of required area usage of approximately 3000 m2 , in an existing chemical

25

complex.

26

A total of 8000 h of continuous manufacturing per year was assumed, as well as an

27

annual operator salary of $52 900 and three shifts per day to guarantee a 24/7 operation. 19



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Table 3: Raw material costs 35 . Chemical

Cost ($ t−1 )

Sodium hydroxide (50 wt%) Methanol Metallic sodium Hydrogen

378 342 2531 0.596a

Price in $Nm−3 calculated from eNm−3 . Standard conditions are 1 atm and 0 ◦C. US$1 = e0.92. Data retrieved from 36 . a

1

COL depends on the number of operators per shift as well as the supervisory and clerical

2

labor costs, which in turn depend on the type and number of equipment units 33 , as COL =

3

$52900 × Fop (6.29 + 0.23Nnp )0.5 . Nnp is the number of non-particulate processing units

4

(excluding e.g., pumps and vessels), and Fop is the number of operators hired for each

5

position in the plant at a given time (4.5, in this case).

6

The method adopted for the depreciation of FCIL was the modified accelerated cost

7

recovery system (MACRS) with a 5 year recovery period, a project life of 10 years, and no

8

salvage value. In addition, two years of construction is assumed, with FCIL distributed as

9

60 % in year one and 40 % in year two. The annual interest rate (AIR) was set at 10 % and

10

the overall income tax rate (t) at 42 % 33 .

11

The unitary costs of the chemical materials involved in the processes are given in Table 3,

12

and the utility costs considered are presented in Table 4. To account for uncertainty and

13

variability in these data and other cost components, Monte-Carlo simulations were carried

14

to quantify the individual and global risks associated with those uncertainties.

15

4.1

16

The capital costs were estimated by sizing and costing the reactor R-501, pumps P-501 and

17

P-502. The reactor was scaled-up using the information available in the literature 10 , where

18

a reactor of 800 gallon filled-up to 60 % is mentioned to produce 2.88 m3 hr−1 of NaOCH3

19

diluted to 25 % in methanol. From this data, the residence time of R-501 is estimated as

Process II

20


Page 20 of 42

Page 21 of 42

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Table 4: Utility costs 33 . Utility

Cost ($ GJ−1 )

Cost ($/common unit)

13.28 16.8 14.2 0.354

$27.7 /1000 kg $0.06 kW h $549 /m3 $14.8 /1000 m3 $36 /t

Low pressure stream (5 barg, 184 ◦C)a Electricity (110 - 440 V) Fuel oil (no. 2) Cooling tower water Waste disposalb a

With credit for power.

b

Solid or liquid. Nonhazardous.

1

τR = (0.6 × VR × 60) /F = (0.6 × 3.03 × 60) /2.88 = 38 min. The reaction volume of R-501

2

is therefore 12 m3 with a reactor height/diameter ratio of 3, as recommended from heuristics

3

in Couper et al. 37 . Table 5 shows the equipment unit sizes and respective capital costs in

4

Process II and III. The equipment in this process uses stainless steel, due to the corrosion

5

effects of the solutions with NaOH and methanol in carbon steel 37 .

6

The costs on an annual basis and by tonne DM of NaOCH3 produced, the utility re-

7

quirements, and sales revenues (Rvn ) in Process II are presented in Table 6. The results

8

show that CRM represents approximately 89 % of COMd , which is $3199 /t DM of NaOCH3

9

or $803 /t of the 25 wt% solution. The utility costs CUT are low compared with COMd , since

10

only cooling water is needed in R-501, fuel oil is used to melt the Na, and electricity is used

11

to power pumps P-501, P-502 and for agitation and auxiliary equipment. The repairs and

12

maintenance costs (0.18FCIL ) are also reduced, due to the simplicity and low number of

13

equipment units required by this process.

14

The break even price of NaOCH3 was estimated by nulling the sum of the cumulative

15

discounted after-tax cash flows at the end of the project-life period, i.e. NPV = 0 or

16

PVR = 1. The value found was $3170 /t DM of NaOCH3 if credits of H2 are considered

17

and $3295 /t DM if not. This value is barely within quotation values found 35 , which vary

18

between 2500 to 3300 /t DM of NaOCH3. However, the capacity of production considered

19

here (5.40 kt DM/year) is lower than the ones of main producers, which often go up to

20

18 kt DM/year. Later in Section 4.3, the influence of the capacity of production on the price

21



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 42

Table 5: Equipment size and capital costs in Processes II and III. Equipmenta Process II P-501 P-502 R-501 P j CBM,j /($K) CTM /($K) CGR /($K) Process III C-501 DRV-501 P-501 REB-501 REB-502 COND-502 H-501 H-502 D-501 D-502 DRUM-502 P j CBM,j /($K) CTM /($K) CGR /($K) a

Type

Centrifugal Centrifugal Agitated jacketed

Power Length Diameter Area CP0 (kW) (m) (m) (m2 ) ($K) 4.0 2.5

6.2

4.3 3.9 91.8

2.1

CBM ($K) 22.6 20.3 367 410 484 689

Centrifugal Electric-Totally enclosed Centrifugal

635 635 1.0

Fixed tubesheet or U-Tube Floating head Double Pipe 15.3 m packing bed 43 Sieve Trays Horizontal

19.5 29.9 2.4

1.68 2.1 0.91

140 328 636 33.9 1.6

286 135 3.6 38.7 57.3 83.4 27.3 3.6 140 246 6.3

1650 202 17.7 180 265 386 127 16.5 469 1070 39.1 4422 5220 6460

The materials of construction for all equipment units are in stainless steel.

22


Page 23 of 42

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Table 6: Manufacturing costs and sales revenues of Process II and III. Process II Annual cost Cost ($K) ($/t a) Materials Sodium metal NaOH Methanol 1.23 CRM Utilities Cooling water Fuel oil Electricity LP steam 1.23 CUT

5836

1081

6671 15384

1235 2849

1 7 3

0.22 1.22 0.55

13

2.46

Waste Waste treatment 1.23CWT 2.76 COL 0.18 FCIL COMd Products Hydrogen NaOCH3 Rvn

Process III Annual cost Cost ($K) ($/t a)

3075 5426 10457

570 1005 1937

43

8

307 2446 3439

57 453 637

211 260

39 48

1752 124 17273

324 23.0 3199

1898 1163 17221

352 215 3189

673 17119,c 17792 17792

125 3170,c 3295 3295

18962 18962

3512 3512

After-tax cash flowd Price of NaOCH3b($/kg) DPBP (years) ROROI (%)

301 0.785,c 0.816 2.3 37.9

1009 1.045 6.0 9.8

of NaOCH3 (DM). b Break even price, i.e. NPV = 0 or PVR = 1. c Without credits of H2 sales. d Non-discounted and in non-depreciation period: (Rvn − COMd ) (1 − t).

a

23



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

of NaOCH3 for both processes is also considered.

2

4.2

3

The equipment units considered in the estimation of the capital costs include the vapor com-

4

pressor (C-501) and its respective drive (DRV-501), the pump (P-501), the heat exchangers

5

(H-501 and H-502), the distillation columns (D-501 and D-502) and their respective reboil-

6

ers (REB-501 and REB-502), the condenser COND-502, and the reflux drum (DRUM-502)

7

of D-502. These were sized using the Icarus system in ASPEN Plus® . D-501 is a struc-

8

tured packing bed distillation column because it operates with low liquid flow rates, has

9

a significant hydraulic capacity, and provides an effective vapor-handling ability avoiding

10

a larger distribution of reaction residence times. D-502 is a sieve tray distillation column,

11

since these are less expensive, have lower maintenance costs, and are suitable for the high

12

liquid flow rate inside this column. All equipment in this process uses stainless steel, due

13

to the corrosion effects of the solutions with NaOH and methanol in carbon steel 37 . The

14

heat exchangers have a carbon steel shell since the low pressure steam used in H-501, H-502,

15

REB-501, REB-502, and the cooling water in COND-501 are not corrosive to this material.

16

The size and respective costs of the equipment units of Process III are presented in Table 5.

17

The total fixed capital costs are $6.46 M, approximately 9 times higher than in Process II,

18

with the bare module costs of C-501 (including DRV-501) and D-502 (including REB-502,

19

COND-502, and DRUM-502) representing about 82 % of the total. The annualized manu-

20

facturing costs and revenues of this process are presented in Table 6.

Process III

21

The estimated break even price of NaOCH3 is $3512 /t DM, which is 10.8 % higher than in

22

Process II when credits for H2 are considered and 6.6 % when those are not accounted. Giving

23

the uncertainty of these predictions, the results indicate that Process II and III are relatively

24

comparable in terms of economic performance for the capacity of production assumed, and

25

that a more detailed analysis would be required to characterize more precisely the differences

26

observed between these alternatives. The lower manufacturing costs of Process III are offset 24


Page 24 of 42

Page 25 of 42

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1

by the higher fixed capital and maintenance costs. The expenses with raw materials (CRM )

2

are $1937 /t DM of NaOCH3, 32 % less than in Process II, and representing 61 % of COMd .

3

As for the utilities costs, these are $637 /t DM of NaOCH3 (20 % of COMd ), considerably

4

higher than in Process II, which are only $2.46 /t DM of NaOCH3. This increase on CUT is

5

mainly associated with the heat and electric power required to recycle and maintain a large

6

excess of methanol in D-501, to maintain a high overall conversion of NaOH.

7

After the characterization of the economic factors for the base case conditions, a sensitiv-

8

ity analysis was considered to elucidate the effect of the D-501 and D-502 operating pressures

9

(respectively, PD501 and PD502 ) on the economic performance of Process III (Section D of

10

the Supporting Information). The results obtained indicate that it is more economic to

11

operate this process at atmospheric pressure, and therefore the operating conditions sug-

12

gested in Guth et al. 14 are close to the best found here. Nevertheless, it is recognized that

13

the application of systematic optimization methodologies as in Neves et al. 38 , possible with

14

the availability of a fully equation-oriented process model, could further improve the loca-

15

tion of the global optimal operating conditions, by refining the unit design procedures while

16

simultaneously considering the effect of all process variables.

17

4.3

18

Figure 4 represents the end of year discounted after-tax cash flows of Process II and III for

19

the break even point, i.e., for NPV = 0 or PVR = 1. The corresponding numerical results

20

are detailed on Table S17 and Table S18 in the Support Information. These results show that

21

the DPBP of Process II is 2.3 years with a ROROI of 37.9 %, while the DPBP of Process III

22

is 6.0 years with a ROROI of 9.8 %. This is mainly due to the larger initial capital investment

23

needed in the latter ($8.62 M) compared to the former ($2.67 M). Therefore, for similar sales

24

volumes and nominal prices of NaOCH3, the initial investment is more readily recovered in

25

Process II and more non-discounted money is generated from the initial capital investment

26

made. Nonetheless, Process III is able to even the NPV of Process II at the end of project

Comparative profitability analysis

25



0

- 2 Project value ($M)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 42

- 4

- 6

Process II Process III 0

2

4

6

8

10

12

Project life (year)

Figure 4: Discounted after-tax cash flows of Processes II and III during project-life period. 1

life, due to its higher discounted annual cash flows. In this study, the discounted cash flow

2

rate of return of both processes equals the AIR (10 %), since NaOCH3 is being priced for the

3

projects at break even conditions.

4

As mentioned earlier, the break even prices for NaOCH3 estimated here are just barely

5

within (Process II) and above (Process III) the quotation margins available. As a result, we

6

studied the influence of the capacity of production of NaOCH3 for both processes between

7

5.40 to 18 kt DM/y, corresponding to 21.6 to 72 kt/y in Process II or 18 to 60 kt/y in Pro-

8

cess III. In Figure 5 it is observed that the break even price of NaOCH3 from in Process II

9

drops 7.9 % to $3033 /tDM, while in Process III the reduction is from 10.9 % to $3128 /t DM.

10

The more significant reduction in Process III is due to the fact that the manufacturing costs

11

COMd are lower in this case, and the capital costs CGR do not increase proportionally to the

12

production volume. In Process II, the primary cost is related with the use of sodium metal,

13

which is directly proportional to the production capacity established. Therefore, as the vol-

14

ume of NaOCH3 produced increases, the break even prices of NaOCH3 in both processes

15

tend to become closer and within the range of $3000 to 3150 /tDM.

26


Page 27 of 42

Process II

3500

Process III NaOCH3 break even price ($/t)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


3400

3300

3200

3100

6000

8000 10 000 12 000 14 000 NaOCH3 capacity of production (t DM/y)

16 000

18 000

Figure 5: Break even prices of NaOCH3 as a function of the annual production capacities. 1

The last stage of this analysis is the evaluation and comparison of the risks associated

2

with Processes II and III, relatively to the uncertainty of key economic factors. The goal is to

3

characterize how much the profitability of each competing process can be affected by varia-

4

tions within the expected range of these parameters. A probabilistic approach to quantify the

5

risk is used, by assigning probability distributions to the key parameter values (triangular,

6

in this case), successively sampling parameter values and computing a dependent function

7

for each scenario generated. The economic parameters considered subject to uncertainty are

8

the FCIL , the price of products, FCIwork , AIR, CRM , and CUT . For each scenario generated,

9

the NPV of each process was calculated and cumulative probability curves (CPV(δ)) were

10

constructed through interpolation. Table 7 gives the parameter ranges and their base val-

11

ues for each process, for the base case conditions considered. To analyze the contribution

12

of the uncertainties regarding each parameter individually and simultaneously, 10 000-point

13

Monte-Carlo (MC) simulations were carried out, using a customized version of the CapCost

14

software 33 .

15

The NPV cumulative distributions of both processes are represented in Figure 6, and a

16

more detailed statistical characterization of these results is presented in Table 8. The graphic 27



1.0

All FCIL Price of product Working capital Income tax rate Interest rate Raw materials Utilities

Cumulative probability

0.8 0.6 0.4 0.2 0.0

- 5

0 Net present value/($M)

5

(a) 1.0

All FCIL Price of product Working capital Income tax rate Interest rate Raw materials Utilities

0.8 Cumulative probability

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 42

0.6 0.4 0.2 0.0 - 8

- 6

- 4

- 2 0 2 Net present value/($M)

4

6

(b)

Figure 6: Cumulative probability against NPV from MC simulations of Process (a) II and (b) III.

28


Page 29 of 42

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 7: Expected variation range of key economic parameters, and respective base values.

FCIL Price of products FCIwork t AIR Raw materials price Utilities

Lower limit

Upper limit

Base value Process II Process III

−20 % −10 % −50 % −20 % −10 % −10 % −10 %

30 % 10 % 10 % 20 % 20 % 15 % 10 %

$689 K $6.46 M $17.79 M $18.96 M $1.38 M $1.56 M 42 % 10 % $12.51 M $8.50 M $10.80 K $2.80 M

1

information indicates that the probability of Process II being profitable is 33.5 %, compared

2

with 40.9 % for Process III, when all uncertain parameters are considered simultaneously.

3

These values are obtained from the cumulative probability curves labeled All, which considers

4

the simultaneous variations in all uncertain parameters. Denoting as p the value of this

5

curve when NPV = 0, then 1 − p represents the probability of the corresponding process

6

being profitable. The uncertainties in CRM and in the price of the product are the main

7

contributors to the overall risks involved in the profitability of both processes. However,

8

the impact of CRM is more severe in Process II where the NPV varies between $4.50 M

9

and −$6.66 M for a uncertainty of −10 % to 15 %, because these represent most of the

10

manufacturing costs and also the fixed capital costs are relatively low. Therefore, in order to

11

maintain Process II profitable, it is necessary to guarantee a secure supply and stable prices

12

of Na. These risks may be avoided by including the production of NaOCH3 in the chemical

13

supply of organometallic producers, where Na can take part in a more diverse portfolio, with

14

complementary applications. Also, the uncertainty in other parameters such as the FCIL

15

or CUT have a negligible effect on the profitability variation of this process, given their low

16

impact on the overall cost structure.

17

In Process III, the uncertainty in CRM is also a main risk for profitability, although

18

with NPV varying less, from $3.02 M to −$4.59 M. As this process uses caustic soda which

19

is almost seven times cheaper than Na, CRM has less importance on the overall process

29



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Table 8: NPV distribution for Processes II and III. Quartiles

Process II Process III NPV/($ M)

25 % 50 % 75 %

< − 3.45 < − 1.72

Process Simulation and Techno-Economic ... - ACS Publications

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