Microfiltration of thin stillage - Semantic Scholar

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Microfiltration of thin stillage: Process simulation and economic 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,* a

University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA National Center for Agricultural Utilization Research, Agricultural Research Service, USDA, 1815 North University Street, Peoria, IL 61604, USA c Animal Sciences, University of Missouri, Columbia, MO 65211, USA b

article info

abstract

Article history:

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

Received 23 October 2009

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

Received in revised form

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

26 July 2010

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

Accepted 4 August 2010

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

Keywords:

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

Corn

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

Dry grind

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

Ethanol

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

Thin stillage

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

Microfiltration

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

Flux

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

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

energy efficiency and increases operating costs through higher steam requirements and increased cleaning of evaporators [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 (240 million gal/yr) to 1064 million L (280 million gal) of thin stillage. Membrane filtration is one method that could lead to improved value of thin stillage and may offer an alternative to evaporation [5e7]. Using membranes, the permeate stream from membrane filtration could be recycled at higher rates

* Corresponding author. Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, 1304 West Pennsylvania Avenue, Urbana, IL 61801, USA. Tel.: þ1 217 265 0697; fax: þ1 217 244 0323. E-mail address: [email protected] (K.D. Rausch). 0961-9534/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.biombioe.2010.08.024

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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 determining the cost of membrane filtration systems [8e10]. Large membrane area results in high capital and operational 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 concentration 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 profile

There 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. Macromolecular solutes, for example proteins, tend to form gels at high concentration. The gel layer can be observed on the membrane surface after ultrafiltration of such solutes. This work 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 optimization

Fig. 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 one membrane is an input stream for the next membrane 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, respectively. For the ith membrane, FFi1 was input, Fpi the permeate and FFi the retentate. At the ith stage, CFi will be defined as ratio of FFi1 to FFi. A1, A2, A3 .. and Ai are the respective areas of membranes. All stages were operated in series with respect to retentate flow, but in parallel with respect to permeate flow. 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 function must 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. area

Development of optimized membrane

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 problems based on MINLP have been solved by several researchers in water desalination and dairy research areas [12e14,24,25]. Most of the researchers chose MINLP solvers such as general process modeling system (gPROMS) or general algebraic modeling system (GAMS) or used solution methodologies such as artificial neural networks (ANN) and some have developed their own

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 solution 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 a þ b lnðCFi Þ

(2)

The objective function is min

N X

Ai

(3)

i¼1

which seeks to minimize the total area of membrane system and is subject to the following constraints: CFi < CFiþ1

(4)

and CF1 CF2 CF3 ...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 FFi1 ¼ FFi þ FPi

FFi1 FFi

FPi Ji

(8)

(9)

Total area for system with N stages AT ¼

N X FPi i¼1

Ji

(12)

and CF1 CF2 CF3 ...CFi ¼

FF FFi

(13)

Eqs. (11) and (12) in (10), we have AT ¼

N X i¼1

3.2.

ðFF ÞðCFi 1Þ ða þ blnðCFi ÞÞPij¼1 CFj

(14)

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 Run N simulation cases (with each case iterating over multiple CF values) For each simulation case Initialize Index ¼ 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.)

Case study

(7)

Area of ith stage Ai ¼

Since CFi ¼ FFi1 =Fi or Fi1 ¼ CFi FFi

4.

CF at ith stage CF ¼

(11)

i¼1

(6)

Permeate flux rate at ith stage Ji ¼ a þ b lnðCFi Þ

N X

ðFFi1 FFi Þ ða þ b lnðCFi ÞÞ

AT ¼

(10)

FFi1, FFi and FPi are feed, concentrate and permeate flow rate, respectively for ith membrane stage, CFi is concentration factor at ith membrane, 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

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 communication, 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 suspended 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

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Table 1 e Chemical composition of thin stillage. Component

Dry basis (%)* 6.5 0.7 3.0 0.8 3.6 1.2 23.5 2.3 16.7 1.6 10.5 0.5

Total Solids (%) Dissolved Solids (%) Suspended Solids Protein Fat Ash *Mean standard deviation.

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 (CF ¼ 8, 15) were chosen. Based on our previous work (unpublished), solids level at CF ¼ 15 should match with the concentrated thin stillage (syrup, 30e35% solids) at ethanol plants. CF ¼ 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 manufacturer (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 mm pore 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 multistage membrane system for 40 MM gal/yr ethanol plant.

Table 3 e Cost and energy comparison per unit area of evaporators and an N stage membrane plant. Unit operation

Value

a

Evaporation Total Capital cost Total Area (664 m2) Energy (1300 kJ/kg water removed) Membrane filtrationb Capital cost (stainless steel) Operating cost Control valves, pipe and fittings Electricity Cleaning Energy (7e9 kJ/kg water removed)

$ 5000/m2 $ 3,320,000

$ 2400/m2 $ 65,000 per stage $ 0.08/kWh $ 10.0/m2/yr 0.5 KW/m2

a Kwiatkowski et al. (2006). b Data obtained from manufacturer.

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 and membrane 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 achieve CF ¼ 2, system was operated under batch concentration mode and permeate was collected. Next run was operated

Input parameters Thin stillage temperature ( C) Initial total solids (%) Final total solids (%) a Total Input stream (gal/day) a

Total Input stream (gal/day) b Total Input stream (gal/day) Concentration Factor a and b values Operating Time (hr/day) Minimum TMP (psi) Average Velocity (ft/sec) Plant operating time (days/yr) Average membrane life (yr) Feed pump efficiency Circulating pump efficiency

75 7 35 405,000 (73.7% of total thin stillage) 550,000 (100% thin stillage) 760,000 (100% thin stillage) 8, 15 135 and 33.3 (determined using experiments) 22 40 15 330 20 82 86

a Kwiatkowski et al. (2006). b Assuming 250 MM gal/yr thin stillage.

N

∑F i =1

st

1 stage

FP1

FF

P1 FP2 FF1

FP i

P2

2nd stage

FF2

FP3

P3

3rd stage

Pi th

i stage

FF3 FFi-1

FFi

Fig. 1 e Multistage system for thin stillage filtration.

pi

117


200

1600

180

1400

Final CF=15

1200

Final CF=8

Total Area (m2)

160 140 Flux

120

CF1

100 80

CF2

60

CF5 CF10 CF12

40 20

1000 800 600 400 200 0

0 0

20

40

60 80 Time (min)

100

120

0

140

Fig. 2 e Flux profile at different input concentrations. Each flux profile continues from preceding CF value for constant TMP and cross flow velocity.

Javg ¼ J0 0:33 ðJ0 JF Þ

(15)

where Javg ¼ average flux rate, J0 ¼ initial flux rate, and JF ¼ Final 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.

2

3

4 5 Number of stages

Jss ¼ 135:7 33:3 lnCF

(16)

Constants values (a ¼ 135.7 and b ¼ 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

a

3,500,000

Total Cost Membrane module cost Additional cost

3,000,000

Depreciation (10 yrs) Membrane replacement Cleaning ($10/m2/yr) Labor and Maintenance (3% of capital cost) Power ($/yr) Total operating cost ($/yr)

Batch ($/yr) 4 stage system ($/yr) 90,497 90,497 7550 57,000

44,517 41,877 3710 37,320

300,000 545,544

146,916 274,340

1,500,000

500,000 0 0

3,500,000

1

2

b

3,000,000

3


Total Cost Additional cost

6

7

8

9

Membrane module cost

2,500,000

Cost ($)

Operating cost

2,000,000

1,000,000

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 4 e Comparison of batch and multistage membrane system’s operating costs at flow rate of 405,000 gal/day and CF [ 15.

8

For a 152 million L/yr ethanol plant, permeate flux rates for system design followed semi logarithmic relationship with CF values as follows

Cost ($)

Economic analysis

7

Results and discussion

2,500,000

5.

6

Fig. 3 e Minimum total areas as a function of number of stages at input stream flow of 760,000 gal/day.

6. with remaining retentate volume left after 1st batch run to concentrate further from CF ¼ 2 to CF ¼ 5 using clean membrane. Likewise, next batch run was operated to achieve CF ¼ 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].

1

2,000,000 1,500,000 1,000,000 500,000 0 0

2


8

10

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).

118


4,000,000

4,000,000 CF=15

a

3,500,000

CF=8

3,000,000

Total Cost ($)

Total Cost ($)

3,500,000

2,500,000 2,000,000 1,500,000

A

3,000,000

2,500,000 2,000,000 1,500,000 500,000

500,000

0 0

0 1

2

3 4 5 Number of stages

6

7

3,500,000

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).

A

2,000 1,800

A (CF=15)

B (CF=15)

1,400

C (CF=15)

A (CF=8)

1,200

B (CF=8)

C (CF=8)

1,000 800 600 400 200 0 1

2

3


6

7

8


9

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).

8

10

b

3,000,000

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 (CF ¼ 15) (Figs. 3 and 4(a)). There was a reduction in area requirement and total cost in initial stages (1 and 2) to achieve CF ¼ 8 compared to CF ¼ 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 multistage membrane system. Three input streams were 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

1,600

2

8

Total Cost ($)

0

Area (m2)

C

1,000,000

1,000,000

0

B

B

C

2,500,000 2,000,000 1,500,000 1,000,000 500,000 0

2


8

10

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 [ 2.89 3 106 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 [ 1.54 3 106, B [ 2.1 3 106 and C [ 2.89 3 106 L/day).

increasing input stream flow rates. About 23 and 47% increment 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 dissolved 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 stream with 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 (CF ¼ 8 to CF ¼ 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 systems were always high. In successive stages, fouling will be prominent due to high CF values and additional cost in terms of pipes, control valves, instrumentation will increase total cost. The optimum number 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


4,000,000

Total Cost ($)

3,500,000

[2]

3,000,000

A

A

B

C

2,500,000

[3] Membranes

2,000,000 1,500,000

[4]

1,000,000 500,000 0 A

A B Feed flow rates (gal/day)

C

Fig. 8 e Comparison of 4 effect evaporator and optimal multistage membrane system total capital costs at different input flow rates ((A [ 1.54 3 106, B [ 2.1 3 106 and C [ 2.89 3 106 L/day) and CF [ 15.

process more than twice the amount of input flow rate an four effect evaporator system could process for the same cost (Fig. 8). Operating cost comparisons showed that there was cost 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.

[5] [6]

[7]

[8] [9] [10] [11]

[12]

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 algorithm structure has been used to optimize area requirement and minimize total capital cost. Total area requirement decreased with number of stages but there was tradeoff between higher capital costs involved at higher number of stages. For target CF ¼ 15, a 5 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 stream flow 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 factors such as permeate flux sensitivity and choosing various input stream tank volumes should also be considered to understand their influence on overall economics.

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20] [21]

[22]

[23]

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