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Microfiltration of thin stillage: Process simulation andeconomic analyses

Amit Arora a, Anupam Seth a, Bruce S. Dien b, Ronald L. Belyea c, Vijay Singh a,M.E. Tumbleson a, Kent D. Rausch a,*aUniversity of Illinois at Urbana-Champaign, Urbana, IL 61801, USAbNational Center for Agricultural Utilization Research, Agricultural Research Service, USDA, 1815 North University Street, Peoria,

IL 61604, USAcAnimal Sciences, University of Missouri, Columbia, MO 65211, USA

a r t i c l e i n f o

Article history:

Received 23 October 2009

Received in revised form

26 July 2010

Accepted 4 August 2010

Keywords:

Corn

Dry grind

Ethanol

Thin stillage

Microfiltration

Flux

* Corresponding author. Agricultural and BioAvenue, Urbana, IL 61801, USA. Tel.: þ1 217

E-mail address: krausch@illinois.edu (K.D0961-9534/$ e see front matter ª 2010 Elsevdoi:10.1016/j.biombioe.2010.08.024

a b s t r a c t

In plant scale operations, multistage membrane systems have been adopted for cost

minimization. We considered design optimization and operation of a continuous micro-

filtration (MF) system for the corn dry grind process. The objectives were to develop

a model to simulate a multistage MF system, optimize area requirements and stages

required for a multistage system and perform economic analysis of a multistage MF system

for a 40 million gal/yr ethanol plant. Total area requirement decreased with number of

stages but there was tradeoff between higher capital costs involved at higher number of

stages. To achieve thin stillage total solids concentration from 7 to 35%, a 5 stage

membrane system was found to be optimum with area requirement of 655 m2 for

minimum cost. Increase in the input stream flow rate from 1.54 � 106 to 2.89 � 106 L/day

significantly increased the total capital cost of the system by 47%. Compared to a single

stage system, an optimal system had a 50% reduction in operating costs. Optimal system

also showed potential to process more than twice the amount of thin stillage compared to

a 4 effect evaporator system for given conditions.

ª 2010 Elsevier Ltd. All rights reserved.

1. Introduction energy efficiency and increases operating costs through

The corn based dry grind process is the most widely used

method in the US for fuel ethanol production. Fermentation of

corn to ethanol produces whole stillage after ethanol is

removed by distillation. Whole stillage is centrifuged to

separate thin stillage from wet grains. Thin stillage contains

5e10% solids and they are concentrated using evaporators.

The process of deposit settling and accumulation on heat

transfer surfaces which reduces heat transfer rates and

increase pressure loss is known as fouling. Fouling decreases

logical Engineering, Univ265 0697; fax: þ1 217 244. Rausch).ier Ltd. All rights reserved

higher steam requirements and increased cleaning of evapo-

rators [1e3]. In the dry grind industry, typically 6e7 L thin

stillage is produced for 1 L of ethanol [4]. Therefore, a typical

152 million L/yr (40 million gal/yr) ethanol plant will produce

about 912 (240million gal/yr) to 1064million L (280million gal)

of thin stillage.

Membrane filtration is one method that could lead to

improved value of thin stillage andmay offer an alternative to

evaporation [5e7]. Using membranes, the permeate stream

from membrane filtration could be recycled at higher rates

ersity of Illinois at Urbana-Champaign, 1304 West Pennsylvania0323.

.

b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 1 3e1 2 0114

within the dry grind process and retentate could be further

dried and fed to animals. Microfiltration (MF) is a less energy

intensive process (7e9 kJ/kg H2O removed) compared to triple

effect evaporation that requires about 1300 kJ/kg H2O [5]. MF

membranes have also been shown to effectively remove

suspended solids from thin stillage stream [6].

The current trend in the membrane market has shown

significant improvements in technical efficiencies of

membrane systems. This could make membranes a cost

competitive alternative to conventional evaporation system.

Permeate flux rate and throughput are critical measures of

membrane performance and play important roles in deter-

mining the cost of membrane filtration systems [8e10].

Large membrane area results in high capital and opera-

tional costs, therefore it is important to minimize capital cost

by altering the operating parameters such as applied pressure

and temperature to the most favorable condition with respect

to flux so that more permeate flux could be generated per unit

area of membrane. Target of achieving higher concentration

factor (CF) requirement implies larger membrane area, thus

significant increase in costs [9,11e14]. CF is defined as ratio of

total initial feed volume (Vo) to the retentate volume (VR) at

time t.

CF ¼ VO=VR (1)

Higher CF refers to higher solids concentration in input

stream. In plant scale operations, multistage systems have

been adopted instead of batch and feed & bleed systems for

area reduction and cost minimization [15e17]. The main task

in optimal design of membrane processes is to ensure

maximum permeate flow, maximize solute rejection, and

minimize capital and operating costs. There is no published

work of optimum membrane design system for thin stillage

filtration. In this study, the objectives were to (1) simulate

a multistage microfiltration system for a 40 million gal/yr

ethanol plant, (2) optimize area requirement and number of

stages for multistage system to achieve target of minimum

cost and (3) evaluate the design under varying final concen-

tration factors and input flow rates.

2. Materials and methods

2.1. Model development and optimization

A wide variety of models are possible, each of which may be

suitable for a different application. A flux prediction model

was used to determine permeate flux rate at various CF values

following methods described by [9,12,13]. The system was

then optimized by minimizing capital and operating cost

based on number of stages and area requirement per stage.

2.1.1. Flux profileThere have been various approaches to describe the mass

transfer dynamics in the membrane separation process

[8,21,22]. The most commonly used are osmotic pressure

model, film theory model and resistance in series model to

predict permeate flux rate across the membrane [7,21,23]. In

membrane separation processes, solutes that are rejected by

the membrane accumulate on the membrane surface. The

concentration of solutes on the membrane surface increases

and increase the resistance against permeate flow. This is

called concentration polarization phenomenon. Several

researchers have correlated permeate flux rate values with CF

which were influenced by the film theory model [12,16,17].

Higher CF values signify concentration of solids at retentate

and decline in flux is a consequence explained by film theory

model. Permeate flux is reduced exponentially with an

increase of solids concentration in input stream. Macromo-

lecular solutes, for example proteins, tend to form gels at high

concentration. The gel layer can be observedon themembrane

surface after ultrafiltration of such solutes. Thiswork followed

previous researchers approach to develop relationship

between flux rate and CF in order to predict flux rates [9,12,13].

2.1.2. Process model optimizationFig. 2 details the standard multistage system configuration

modeled in this study as described by [8,9,17]. The system

comprises an N stage membrane module system where each

membrane unit is feed & bleed system connected in series and

retentate collected from onemembrane is an input stream for

the nextmembrane and so on. The input stream had flow rate

FF and initial CF ¼ 1, since there was no concentration before

the stream passed through the membrane. Permeate and

retentate flow rates were designated as FP1 and FF1, respec-

tively. For the ith membrane, FFi�1 was input, Fpi the permeate

and FFi the retentate. At the ith stage, CFiwill be definedas ratio

of FFi�1 to FFi. A1, A2, A3 .. and Ai are the respective areas of

membranes. All stages were operated in series with respect to

retentateflow, but inparallelwith respect to permeateflow.All

permeate streams are collected in the permeate product and

the retentate leaving the last stage is the retentate product.

The approach developed for this work was to use CF as

a basis of calculation. In order to optimize the design, an

objective functionmust be defined. In this case, a desired CF is

specified. Therefore, to achieve a final CF value, the number of

stages and corresponding areas needed to be determined.

They should be chosen in such a way that total area will be

minimized. The optimal design and operation of multistage

membrane system for case study has been found using the

procedure described in the model development section. The

objective function Amin was evaluated against the number of

stages.

3. Development of optimized membranearea

3.1. Solution methodology

The optimization design problem is formulated as mixed

integer nonlinear programming (MINLP) for minimizing total

area subject to certain constraints. Similar optimization prob-

lemsbasedonMINLPhavebeensolvedbyseveral researchers in

water desalinationanddairy researchareas [12e14,24,25].Most

of the researchers choseMINLP solvers such as general process

modeling system (gPROMS) or general algebraic modeling

system(GAMS)orusedsolutionmethodologiessuchasartificial

neural networks (ANN) and some have developed their own

b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 1 3e1 2 0 115

algorithm to solve the problem. Also, there are other sources

available for users to solve problems using online servers for

optimization [26]. In this study, a MINLP problem has been

solved by using an algorithm to exhaustively search the solu-

tion space.

The decision variables for the design would be CF, number

of stages (N) and membrane areas (Ai) values. These are the

unknown parameters and CF values should be chosen in such

a way that area corresponding to each CF value will be

minimum i.e, find CFi values in such a way that total area (AT)

is minimized. Input stream flow rate (FF), total target CF (FF/Fn)

and constants for flux prediction (a and b) are known

parameters.

where permeate flux rate (Ji) is

Ji ¼ aþ b � lnðCFiÞ (2)

The objective function is

minXN

i¼1

Ai (3)

which seeks to minimize the total area of membrane system

and is subject to the following constraints:

CFi < CFiþ1 (4)

and

CF1CF2CF3...CFi ¼ FF

FFi(5)

it can be solved readily for 1e2 stages using a straightforward

derivative method, but as the number of stages will increase,

complexity will increase proportionately due to the nonlinear

constrained nature of the problem. Therefore, an iterative

method using computational programming must be used to

solve the problem [14]. The following equations were used to

find an expression for optimum total area (AT).

Conservation of mass at ith stage

FFi�1 ¼ FFi þ FPi (6)

Permeate flux rate at ith stage

Ji ¼ aþ b � lnðCFiÞ (7)

CF at ith stage

CF ¼ FFi�1

FFi(8)

Area of ith stage

Ai ¼ FPi

Ji(9)

Total area for system with N stages

AT ¼XN

i¼1

FPi

Ji(10)

FFi�1, FFi and FPi are feed, concentrate and permeate flow rate,

respectively for ith membrane stage, CFi is concentration

factor at ithmembrane, Ji is permeate flux rate, Ai is area of ith

membrane, AT is total area of membranes, a and b are

constants, N is number of stages.

Substituting Eq. (5) into (8), we have

AT ¼XN ðFFi�1 � FFiÞ (11)

i¼1

ðaþ b � lnðCFiÞÞ

Since CFi ¼ FFi�1=Fi or Fi�1 ¼ CFiFFi (12)

and

CF1CF2CF3...CFi ¼ FF

FFi(13)

Eqs. (11) and (12) in (10), we have

AT ¼XN

i¼1

ðFFÞðCFi � 1Þðaþ blnðCFiÞÞPi

j¼1CFj

(14)

3.2. Algorithm

The optimum was chosen from the entire dataset based on

the minimum area and minimum cost for the system under

given overall concentration factor specified (CFi). The program

will determine the minimal area distribution that meets the

design specified requirement as given in equations. The

program was written in Visual Cþþ language.

Algorithm Optimized Area Computation.

Initialize Area and MinArea vector

RunN simulation cases (with each case iterating overmultiple

CF values)

For each simulation case

Initialize Index¼ 0

Initialize CF vector

CF[0]¼ 1

CF[i]¼ 2 for all i> 0

While(Algorithm Generate next CF vector returns Success)

Compute local area

Add to total area

From the Nmax solutions overall simulations, chose the best

overall solution (AT min.)

4. Case study

The proposed methodology for multistage MF system design

has been applied to a 40 million gal/yr dry grind ethanol plant

in order to find an alternative to existing evaporator unit for

thin stillage concentration. Thin stillage composition has

been presented in Table 1. Input parameters needed for the

study were taken from various sources and compiled in Table

2. Commercial thin stillage temperature varies from 70 to

85 �C and total solids vary from 5 to 10% depending upon plant

operating conditions (Rausch et al., 2003, Rausch and Belyea

2006). Total thin stillage production for 152 million L/yr plant

varies from 912 to 1064 million L/yr (4, personal communica-

tion, Aventine Renewables Inc.). Kwiatkowski et al. [27] used

4.5 L thin stillage for 1 L of ethanol in dry grind process

modeling. Thin stillage input flow rates from 4.5 to 6 L per 1 L

of ethanol were considered in this study. Dissolved and sus-

pended solids in thin stillage vary and their proportion may

affect the evaporation process. Higher dissolved solids level

reduces evaporator efficiency as it makes the thin stillage

more viscous [28]. Higher dissolved to suspended solids ratio

Table 3 e Cost and energy comparison per unit area ofevaporators and an N stage membrane plant.

Unit operation Value

Evaporationa

Total Capital cost $ 5000/m2

Total Area (664 m2) $ 3,320,000

Energy (1300 kJ/kg water removed)

Membrane filtrationb

Capital cost (stainless steel) $ 2400/m2

Operating cost

Control valves, pipe and fittings $ 65,000 per stage

Electricity $ 0.08/kWh

Cleaning $ 10.0/m2/yr

Energy (7e9 kJ/kg water removed) 0.5 KW/m2

a Kwiatkowski et al. (2006).

b Data obtained from manufacturer.

Table 1 e Chemical composition of thin stillage.

Component Dry basis (%)*

Total Solids (%) 6.5� 0.7

Dissolved Solids (%) 3.0� 0.8

Suspended Solids 3.6� 1.2

Protein 23.5� 2.3

Fat 16.7� 1.6

Ash 10.5� 0.5

*Mean � standard deviation.

b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 1 3e1 2 0116

may affect target CF values as they will vary depending upon

amount of dissolved and suspended solids present in the

input stream. In order to evaluate the effect of final CF and

area requirement on total cost, two CF values (CF¼ 8, 15) were

chosen. Based on our previous work (unpublished), solids

level at CF¼ 15 should match with the concentrated thin

stillage (syrup, 30e35% solids) at ethanol plants. CF¼ 8 will

show how variation in thin stillage dissolved solids affects

thin stillage filtration. Other operating parameters for

membrane have been suggested by the membrane manufac-

turer (Tables 2 and 3).

4.1. Experimental material

Thin stillage was collected from a commercial dry grind

ethanol facility. One 500 mL sample from each batch was

analyzed for total nitrogen (TN), ash and fat content using

standard methods [18]. Total solids contents of thin stillage

were determined using a two stage oven method [19]. Thin

stillage samples were analyzed for soluble or dissolved solids

measurement. Samples (25 mL) of thin stillage in triplicate

were filtered through a 1 mmpore size A/E glass fiber filter (Pall

Corporation, East Hills, NY) and dried to constant weight at

180 �C [20].

Table 2 e Summary of input parameters of multistagemembrane system for 40 MM gal/yr ethanol plant.

Input parameters

Thin stillage temperature (�C) 75

Initial total solids (%) 7

Final total solids (%) 35aTotal Input stream (gal/day) 405,000 (73.7% of total

thin stillage)aTotal Input stream (gal/day) 550,000 (100% thin stillage)bTotal Input stream (gal/day) 760,000 (100% thin stillage)

Concentration Factor 8, 15

a and b values 135 and 33.3 (determined using

experiments)

Operating Time (hr/day) 22

Minimum TMP (psi) 40

Average Velocity (ft/sec) 15

Plant operating time (days/yr) 330

Average membrane life (yr) 20

Feed pump efficiency 82

Circulating pump efficiency 86

a Kwiatkowski et al. (2006).

b Assuming 250 MM gal/yr thin stillage.

4.2. Microfiltration experiment

Thin stillage filtration was conducted to observe relationships

between permeate flux rates and concentration factors so that

constants such as a and b could be determined. These

constants cannot be predicted by the model as it largely

depends on feed stream composition andmembrane type and

operating conditions. For filtration study, a tubular stainless

steel MF module with 0.1 mm pore size and area of 0.28 m2

(Scepter model, Graver Technologies, Glasgow, DE) was used.

The material that passed through the membrane was termed

permeate and the material that was retained and returned to

tank was termed retentate. Permeate was collected during

batch concentration and expressed as LMH (liter/m2hr) until

the desired concentration factor was reached. Membrane

system was initially operated in recycle mode to determine

steady state at constant input stream concentration.

To determine constants a and b for eq. (14), experiments

were conducted at different input stream concentrations to

determine flux profiles at various concentrations. For

example, first run was in recycle mode i.e. at CF ¼ 1. To ach-

ieve CF¼ 2, system was operated under batch concentration

mode and permeate was collected. Next run was operated

FF

FF1

FP1

FP2 FP3

FF2

FF3

FPi

FFi-1

FFi

∑=

N

ipiF

1

1st stage

2nd stage

3rd stage

ith stage

P1

P2

P3

Pi

Fig. 1 e Multistage system for thin stillage filtration.

0

20

40

60

80

100

120

140

160

180

200

0 20 40 60 80 100 120 140Time (min)

xulF

CF1

CF2

CF5CF10CF12

Fig. 2 e Flux profile at different input concentrations. Each

flux profile continues from preceding CF value for constant

TMP and cross flow velocity.

0

200

400

600

800

1000

1200

1400

1600

0 1 2 3 4 5 6 7 8Number of stages

m(aer

AlatoT

2 )

Final CF=15

Final CF=8

Fig. 3 e Minimum total areas as a function of number of

stages at input stream flow of 760,000 gal/day.

3,000,000

3,500,000

Total Cost

Membrane module cost

a

b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 1 3e1 2 0 117

with remaining retentate volume left after 1st batch run to

concentrate further from CF ¼ 2 to CF¼ 5 using clean

membrane. Likewise, next batch run was operated to achieve

CF¼ 10 and so on (Fig. 1).

Average flux rates (Javg) for each case was obtained by

following equation which hold true when there is a linear

relationship exist between flux rate and logarithmic CF values

[17].

Javg ¼ J0 � 0:33 � ðJ0 � JFÞ (15)

where Javg¼ average flux rate, J0¼ initial flux rate, and JF¼ Fi-

nal flux rate at given CF value.

Relationship between average flux values and CF values

was established to determine a and b values for the given

multistage system as described by [16]. These constants were

then used in the model for optimizing the area.

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

0 1 2 3 4 5 6 7 8 9Number of stages

)$(tsoC

Additional cost

3,000,000

3,500,000

Total Cost Membrane module cost

Additional cost

b

5. Economic analysis

Capital and operating costs involved in evaporation and

filtration of thin stillage from a 40 million gal/yr ethanol plant

were considered in the analyses (Table 4). Capital cost includes

the costs of membranes, housing and pumps while operating

costs include piping and instrumentation, electricity

consumption in the pumps and costs of cleaning. For cleaning

membranes, 2.0 h/day cleaning time was considered [14,16].

Therefore, effective separation time used was 22.0 h/day.

Table 4e Comparison of batch andmultistagemembranesystem’s operating costs at flow rate of 405,000 gal/dayand CF[ 15.

Operating cost Batch ($/yr) 4 stage system ($/yr)

Depreciation (10 yrs) 90,497 44,517

Membrane replacement 90,497 41,877

Cleaning ($10/m2/yr) 7550 3710

Labor and Maintenance

(3% of capital cost)

57,000 37,320

Power ($/yr) 300,000 146,916

Total operating cost ($/yr) 545,544 274,340

6. Results and discussion

For a 152 million L/yr ethanol plant, permeate flux rates for

system design followed semi logarithmic relationship with CF

values as follows

Jss ¼ 135:7� 33:3 � lnCF (16)

Constants values (a¼ 135.7 and b¼ 33.3) found in (eq. (15))

were used for area optimization. Optimized process model

equation has already been presented in model development

section (Eq. (14)).

As the number of stages increased from 1 to N, total area

requirement decreased. A 50% reduction in area was observed

when increasing stages from 1 to 4 (Fig. 3). There was further

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

0 2 4 6 8 10Number of stages

)$(tsoC

Fig. 4 e (a). Total capital cost as a function of number of

stages to achieve CF[ 15 at input stream flow rate of

2.89 3 106 L/day (760,000 gal/day). (b). Total capital cost as

a function of number of stages to achieve CF[ 8 at flow

rate of 2.89 3 106 L/day (760,000 gal/day).

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

0 1 2 3 4 5 6 7 8

Number of stages

)$(tsoClato

T

CF=15 CF=8

Fig. 5 e Comparison of total cost to achieve CF[ 15 and

CF[ 8 at input stream flow rate of 2.89 3 106 L/day

(760,000 gal/day).

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

0 2 4 6 8 10

)$(tsoClatoT

Number of stages

A B C

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

0 2 4 6 8 10Number of stages

)$(tsoClato

T

A B C

a

b

Fig. 7 e (a). Total capital costs at different input flow rates

and number of stages to achieve CF[ 15 at different input

flow rates (A[ 1.54 3 106, B[ 2.1 3 106 and C

[ 2.893 106 L/day). (b). Total capital costs at different input

flow rates and number of stages to achieve CF[ 8 at

different input flow rates ((A[ 1.543 106, B[ 2.13 106 and

C[ 2.89 3 106 L/day).

b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 1 3e1 2 0118

decrement in area on increasing the number of stages, but

decrease was relatively small for more than 4 stages.

There was a tradeoff between higher capital cost involved

at higher number of stages and high cost due to larger area for

lower number of stages. Cleaning, pipe, control valves and

instrumentation costs increased linearly with the number of

stages (Fig. 4(a) and (b)), thus the total cost of multistage

system increased after attaining global minima (Fig. 5). Thus,

stage 5 gave minimum cost for optimized area (655 m2) to

achieve target condition (CF¼ 15) (Figs. 3 and 4(a)). There was

a reduction in area requirement and total cost in initial stages

(1 and 2) to achieve CF¼ 8 compared to CF¼ 15 but later cost

differences dropped at higher stages (Figs. 4(a) and 5).

Optimum area requirement reduced from 655 to 631 m2 by

reducing CF value from 15 to 8 (Fig. 3).

In Figs. 5e7(a, b), effects of input flow rates on area

requirement and total cost are presented for up to eight stages

of the optimal solution for two CF values. Input stream flow

rate (FF) had the greatest impact on the total capital cost of the

multistagemembrane system. Three input streamswere used

in the sensitivity analysis with flow rates of 1.54 � 106,

2.1� 106 and 2.89� 106 L/day (405,000, 550,000 and 760,000 gal/

day) respectively. Total cost increased significantly by

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

0 1 2 3 4 5 6 7 8 9Number of stages

m(aer

A2 )

A (CF=15) B (CF=15)

C (CF=15) A (CF=8)

B (CF=8) C (CF=8)

Fig. 6 e Minimum area required to achieve CF values 8 and

15 at different input stream flow rates (A, B and C

correspond to input flow of 1.54 3 106, 2.1 3 106 and

2.89 3 106 L/day).

increasing input stream flow rates. About 23 and 47% incre-

ment in total cost was observed by increasing flow rate from

1.54 � 106 (405,000 gal/day) to 2.1 � 106 (550,000 gal/day) and

2.89 � 106 L/day (760,000 gal/day).

Changes in thin stillage composition may also impact the

cost of the process. In this case, we evaluated impact of dis-

solved to suspended solids ratio on cost. Data reported on

dissolved solids in previous study showed higher dissolved

solids than suspended solids [29]. For thin stillage streamwith

total solids of 7% and high dissolved solids to suspended solid

ratio, target CF value will go up to achieve same solids

concentration at retentate. For example, increase in dissolved

solids from 2.0 to 5.0% will require significant increase the

target CF value (CF¼ 8 to CF¼ 15). At higher CF values there

were higher total costs for stages 1 and 2 but higher CF values

did not have an impact on cost for N� 3 stages. It can be

observed how required areas and total costs in 1 and 2 stage

systemswere always high. In successive stages, foulingwill be

prominent due to high CF values and additional cost in terms

of pipes, control valves, instrumentation will increase total

cost. The optimumnumber of stages at higher input flow rates

(A and B) was four whereas three stages were optimum for

lower input flow rate C (Fig. 7(a) and (b)).

Total cost comparison of optimal membrane system with

evaporator suggests that a membrane system could possibly

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

A A B CFeed flow rates (gal/day)

Tot

al C

ost (

$)

A A B C

Membranes

Fig. 8 e Comparison of 4 effect evaporator and optimal

multistage membrane system total capital costs at

different input flow rates ((A[ 1.543 106, B[ 2.13 106 and

C[ 2.89 3 106 L/day) and CF[ 15.

b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 1 3e1 2 0 119

processmore than twice the amount of input flow rate an four

effect evaporator system could process for the same cost

(Fig. 8).Operatingcost comparisonsshowedthat therewascost

reduction of 50% by choosing a four stage system as compared

to single stage system (Table 3). In this study, cleaning cost of

evaporator has not been considered which if included could

further increase the total cost of evaporation process.

7. Conclusions

The optimum microfiltration multistage system has been

modeled for a 152 million L/yr ethanol plant and presented in

this work. The objective function Amin was evaluated against

number of stages. A simplified higher base algorithmstructure

has been used to optimize area requirement and minimize

total capital cost. Total area requirement decreased with

numberof stagesbut therewas tradeoff betweenhigher capital

costs involvedathighernumberof stages. For targetCF¼ 15,a5

stage membrane system was found to be optimum with area

requirement of 655 m2 for minimum cost. Reducing target CF

value from 15 to 8 changed optimum area requirement from

655 to 631 m2 but no significant effect was observed in total

capital costs. Therefore, for an optimized membrane system,

the thin stillage dissolved to suspended solids ratio did not

have affect on total cost. Increase in the input streamflow rate

from 1.54 � 106 to 2.89 � 106 L/day significantly increased the

total capital cost of the system by 47%. Comparison of single

stage system with optimal system showed 50% reduction in

operating cost. Optimal system also showed potential to

process more than twice the amount of input flow rate

compared to the four effect evaporator system for given

conditions.Additional factorssuchaspermeatefluxsensitivity

and choosing various input stream tank volumes should also

be considered to understand their influence on overall

economics.

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