Simulation and optimisation of extractive distillation with water as solvent

Simulation and optimisation of extractive distillation with water as solvent

Chemical Engineering and Processing 44 (2005) 345–351 Simulation and optimisation of extractive distillation with water as solvent Paul Langston∗ , N...

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Chemical Engineering and Processing 44 (2005) 345–351

Simulation and optimisation of extractive distillation with water as solvent Paul Langston∗ , Nidal Hilal, Stephen Shingfield, Simon Webb School of Chemical, Environmental and Mining Engineering, The University of Nottingham, University Park, Nottingham NG7 2RD, UK Received 7 August 2003; received in revised form 16 December 2003; accepted 17 May 2004 Available online 17 July 2004

Abstract Acetone–methanol, methyl acetate–methanol and methanol–chloroform binary extractive single column distillation systems were simulated with the HYSYS software platform, to investigate the effects of solvent feed entry stages, solvent split stream feed and solvent condition on the separation. Water was used in all of the simulations as the solvent. The simulations supported data and findings from experimental column studies of the same systems. A rigorous simulation of the acetone–methanol system including a secondary stripping column and recycle loop was established to simulate an industrially relevant situation. This simulation enabled an economic evaluation of the process to be made. It was found for feed mixtures containing 25, 50 and 75 mol% methanol, the optimum reflux ratios were found to be 3.5, 3.5 and 4.2, respectively. As a consequence one column design could separate binary feed of varying composition between 25 and 75% methanol. The optimum number of ideal stages for the primary column for an equimolar binary feed was determined to be 73. When maintaining a constant solvent flow, the distance between the split feed entry stages had no effect on the economic potential (EP) of the system. © 2004 Elsevier B.V. All rights reserved. Keywords: Extractive distillation; Simulation; Optimisation; HYSYS

1. Introduction Azeotropes are complex, non-ideal mixtures that occur when the components of the mixture have low relative volatilities. The components of these mixtures are very difficult and hence expensive to separate. They can be separated effectively by means of extractive distillation [1,2] whereby the addition of a solvent is made to a distillation column. The solvent acts to increase the relative volatility of the mixture by increasing the activities of the components, as given in the non-ideal binary component mixture relationship: αAB =

γA p0A γB p0B

(1)

∗ Corresponding author. Tel.: +44 115 951 4081; fax: +44 116 951 4115. E-mail address: [email protected] (P. Langston).

0255-2701/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.cep.2004.05.008

where: γ A and γ B are the activity coefficients of components A and B and p0A and p0B are the vapour pressures of components A and B. As the relative volatility of the mixture is raised; the light key can be collected from the top of the distillation column and the mixture of heavy key and solvent retrieved from the column bottom product for subsequent separation. Extractive distillation has found a vast array of diverse applications from the separation of organic compounds in cigarette smoke [3] to the separation of volatile compounds from fruit [4]. Various areas of extractive distillation systems have been investigated previously, such as solvent selection methods [5–8], the development of new extractive distillation systems [9] and the introduction of a salt to the solvent to improve the separation [10–15]. However, there are few publications on the computer simulation of extractive distillation and in some the software used is now outdated [16]. The only other work attempting to simulate and optimise such extractive distillation systems [17] has concentrated on reducing the consumption

P. Langston et al. / Chemical Engineering and Processing 44 (2005) 345–351

of extractive agent (solvent) within the following extractive (binary) and auto-extractive (ternary) distillation systems: • acetone–methanol and water; • methyl acetate–methanol and water; • methanol–chloroform and water. This particular study showed that by increasing the distance between the column process feed and the extractive solvent feed lead to a 35–40% reduction in consumption of the solvent. It was also shown that splitting the solvent feed into two streams into the column lead to a further 25–30% reduction in solvent consumption. These results were confirmed via a HYSYS simulation. Similar work [18] has concentrated on the acetone– methanol–water system, confirming that an increase in the distance between the binary mixture feed and solvent feed increased the column’s performance. The work also investigated the effect of solvent temperature and condition on the quality of product. The study within this paper is closely related to the experimental studies of the three aforementioned binary component extractive distillation systems [17,18]. This simulation based study focuses on simulating the experimental extractive distillation systems (acetone–methanol and water, methyl acetate–methanol and water and methanol–chloroform and water) more thoroughly within the HYSYS simulation package. Thus once established these simulations were then used to economically optimise particular column designs for a specific component separation. The simulations were undertaken with the HYSYS model version 2.1 under license from Hyprotech [19]. The results from the study will provide basic design information in applications associated with extractive distillation.

2. Simulation of primary column operation 2.1. Property package for HYSYS The Van Laar fluid package was used previously [17] in order to simulate the acetone–methanol component system. This fluid package was unable to simulate the other two component systems: acetone–methyl acetate and methanol–chloroform. A different package was required which could simulate all three systems as accurately as the Van Laar package managed the acetone–methanol system. A number of different packages were examined and the Wilson package was found to be the most suitable; it could simulate all three component systems, illustrating good agreement with the acetone–methanol Van Laar data from the previous study (see Fig. 1).

0.2 Top Product Mole Fraction

346

Methanol Wilson Water Wilson Methanol Van Laar Water Van Laar

0.15

0.1

0.05

0

2

3

4 5 6 7 Feed entry stage along column

8

9

Fig. 1. Agreement of Van Laar fluid package results with those derived with Wilson fluid package. Table 1 Primary column principal data Column simulation parameter

Value

Feed flowrate, F (kmol/h) Reflux ratio, R Distillate flowrate, D (kmol/h) Number of ideal stages Solvent/feed flow, q

100 4 47.8 8 4

HYSYS by eight ideal stages. The three systems considered in this study were simulated with the same basic data, shown within Table 1. The solvent to feed ratio (q) was kept at 4.0 for all the simulations as had been discovered previously [17], this was the minimum ratio that enabled a successful separation. 2.3. Effect of feed position of extractive agent The effect of the entry stages of the solvent feed on the separation was then investigated utilising these three established simulations. To do this the binary feed position was maintained at tray 2, whilst the solvent feed stages was varied from stages 3 to 8 (see Fig. 2). For all three systems it

2.2. Simulation data The previous study [17], showed that the experimental packed column could be reasonably simulated within

Fig. 2. HYSYS Process Flow Diagram for single column, binary feed entering at stage 2 and solvent (water) entering at stage 8.

P. Langston et al. / Chemical Engineering and Processing 44 (2005) 345–351

0.2

Top Product Mole Fraction

was found that the concentration of the more volatile component in the top product decreased with an increase in solvent entry feed stage. This agreed with the all three of the experimental systems studied previously [17]. However if the solvent entry stage was near the top of the column, an increased concentration of solvent within the top product was observed. Thus the optimum feed position for the solvent within the column was determined to be the stage whereby the total impurity concentration was at a minimum (see Fig. 3a–c).

347

0.15

0.1 Methanol Water Total Impurity

0.05

0

2.4. Split feed of solvent

2

3

4

5 6 7 Entrystage along column

8

9

(a)

The three single column simulations were then adapted whilst keeping all other parameters constant to simulate equal-split feed extractive distillation, in order to investigate its effect on the top product composition of each component system. This investigation consisted of the independent variation of two parameters: the gap between the two solvent entry streams of equal flowrate and the entry stage at which the upper split solvent feed stream entered the column. This study provided an optimum entry stage and solvent split distance for each of the three component systems under study. The results of both investigations are given in Table 2.

Top Product Mole Fraction

0.18 0.16 0.14 0.12 0.1 Methanol Water Total Impurity

0.08 0.06 0.04 0.02 0 2

2.5. Effect of solvent temperature, feed phase and feed temperature

4

5 6 7 Stage along column

8

9

(b) 0.45 Top Product Mole Fraction

It has been shown [18] that when using an extractive distillation column to separate methanol and acetone using water as the solvent, the solvent temperature and its phase (liquid, two phase or gas) had no effect on the top product composition. The three initial eight stages HYSYS simulations were thus used to vary the feed temperature and similarly solvent temperature. Figs. 4–6 illustrate the effects are similar to those discovered previously [18] for the acetone–methanol component system. The trends are similar for each of the three different component systems simulated. Both binary feed temperature, solvent feed temperature and binary feed phase appear to have no significant effect on the separation in any of the three extractive distillation component systems simulated. With the large reflux ratio the conditions and component relative volatility in the column have not changed significantly. The condenser and reboiler duties (not shown) change as expected.

3

0.4 0.35 0.3 0.25 0.2 0.15

Chloroform Water Total Impurity

0.1 0.05 0

2

3

4

5 6 7 Stage along the column

8

9

(c)

Fig. 3. (a) Top product composition against solvent entry feed stage for acetone–methanol system. (b) Top product composition against solvent entry feed stage for methyl acetate–methanol system. (c) Top product composition against solvent entry feed stage for methanol–chloroform system.

Table 2 Optimum solvent equal-split feed entry stages and optimum split feed stage gaps for each system System Optimum Optimum Optimum Optimum Optimum

single feed entry stage entry stages with split gap of 1 stage entry stages with split gap of 2 stages entry stages with split gap of 3 stages split distance (stages)

Acetone–methanol

Methyl acetate–methanol

Methanol–cloroform

5 4 +5 3 +5 3 +6 2

5 4+5 3 +5 3 +6 1

3 3 +4 3 +5 3 +6 1

348

P. Langston et al. / Chemical Engineering and Processing 44 (2005) 345–351

Top Product Mole Fraction

0.09 0.08 0.07 0.06 0.05

Methanol Water

0.04 0.03 0.02 0.01 0 0

10

20

30 40 50 Solvent temperature oC

60

70

Fig. 4. Effect of solvent temperature on acetone–methanol separation.

Top Product Mole Fraction

0.09 0.08 0.07 0.06

Methanol Water

0.05 0.04 0.03 0.02 0.01 0

0

10

20

30 40 50 Binary feedtemperature oC

60

70

Fig. 5. Effect of binary feed temperature on acetone–methanol separation.

TopPeoduct Mole Fraction

0.12 0.1 0.08 Methanol Water

0.06

The second column for this system was initially set up using the short-cut column design facility to obtain an initial estimate for the number of trays required and the reflux ratio needed in the column. The column was then established with the rigorous column facility and converged successfully. The number of trays and the feed position in the column were then adjusted to enable the column to produce an industrially useful top product of 99.9% methanol and a bottom product containing less than 0.001% water. Thirty-four ideal trays were found to be the optimum, with the feed entering at the 18th stage from the bottom, using a reflux ratio of 3.86. A similar process was carried out in the first column to increase the concentration of the acetone to 99%. A very small amount of solvent make up was required to maintain a q value of 4, although no recycle unit was used within the simulation. Initially the column was set up at atmospheric pressure allowing a column pressure drop of approximately 2 kPa per tray. A pump was installed to pump the binary feed stream into the first column to prevent it from entering the column below the column feed stage pressure. Fig. 7 displays the HYSYS economic simulation flowsheet and Table 3 shows the simulation data produced. The minimum economic potential (EP) was used as the optimisation parameter, using Eq. (2) for EP [20]: EP = Cv + Cf + (ir + im )FC

(2)

where Cv is process variable costs; mostly annual utility consumption; Cf is annual fixed costs i.e. maintenance and wages; FC is fixed capital investment; ir is fixed capital recovery rate applied to FC; im is minimum acceptable rate of return on FC. In this optimisation Cf was assumed to be 10% of FC, and ir + im was assumed to be 25% of FC hence Eq. (2) could be rewritten:

0.04

EP = Cv + (0.35)FC

0.02

however with the devised simulation producing high concentrations of commercially viable products; revenue, R could be obtained from the plant and hence plays a major part in the economic analysis. Thus Eq. (4) was used in order to determine maximum EP as the optimisation parameter in this study:

0

1

2

3

4

Case

Fig. 6. Effect of binary feed phase on acetone–methanol separation: case (1) sub-cooled; case (2) bubble point; case (3) two-phase; case (4) dew point.

3. Economic evaluation of acetone–methanol system 3.1. Economic analysis-procedure In order to perform an economic analysis, simulations producing products of commercially useful concentrations were required and a second column was needed to separate the bottom product from the first column, producing high concentrations of both methanol and water. Low impurity water is needed for the recycle, so as not to adversely affect the performance of the primary column.

EP = R − (Cv + 0.35FC)

(3)

(4)

all of the economic data for the analysis was obtained from Ulrich [21] and the Fisher Scientific Fine Chemical Catalogue [22]. A limitation of the economic optimisation research is that very small variations in product composition affect the overall revenue produced and hence the final economic potential of that particular case. Hence if this HYSYS simulation were to be used to improve an existing column or group of columns via a retrofit, careful analysis would be required before hand to ascertain or estimate the agreement of the simulation with actual plant data.

P. Langston et al. / Chemical Engineering and Processing 44 (2005) 345–351

349

Fig. 7. HYSYS process flowsheet of economic simulation. Table 3 Datasheet from the economic simulation Stream

Binary feed

2

Acetone

4

Methanol

Water

Water recirc1

Make up water

Flowrate (kmol/hr) Temperature (o C) M.F acetone M.F methanol M.F water

100.00 25.0 0.5000 0.0000 0.5000

400.00 60.0 0.0000 0.9990 0.0001

50.10 55.7 0.9970 0.0018 0.0011

44.99 101.6 0.0001 0.8888 0.1110

50.00 64.1 0.0009 0.0000 0.9990

399.90 115.2 0.0000 0.9990 0.0001

399.90 60.0 0.0000 0.9990 0.0001

0.09 25.0 0.0000 1.0000 0.0000

3.2. Optimum primary column reflux ratio The simulation was subsequently used in order to evaluate the economic effect of varying the reflux ratio of the primary column. The secondary column was operated under constant reflux ratio. The number of trays in each column was kept constant. The economic potential relationship (Eq. (4)) was subsequently utilised to provide an economic potential against primary column reflux ratio plot (Fig. 8). 50:50, 25:75 and 75:25 ratio combinations of both components were modelled within the analysis. The optimum primary column reflux ratios are given within Table 4 for different feed compositions. These are very close indeed and suggest that any binary feed from within the range 25 to 75% acetone–methanol could be separated within the same plant, with little loss in economic potential as a consequence, and hence no requirement for plant modifications to process a different feed composition.

8.00000 Economic Potential (£10^8)

Several simplifying assumptions were made when establishing the economic analysis of the HYSYS simulation. It was assumed that within the two reboilers all heating energy was provided from the condensing of steam alone, the effects of sensible heating were considered negligible. This appeared plausible enough to provide heat duty data from which to obtain a capital investment estimate. In addition a heat loss model was not assigned to any of the process vessels or equipment; this again seemed a reasonable assumption considering the level of accuracy used throughout during the fixed cost estimation.

7.00000 6.00000

50% Methanol50% Acetone 75% Methanol25% Acetone 25% Methanol75% Acetone

5.00000 4.00000 3.00000 2.00000 1.00000 0.00000 0 -1.00000

1

2

3

4

5

6

7

8

9

10

11

Reflux Ratio

Fig. 8. Economic potential against primary column reflux ratio for three binary feed composition cases.

3.3. Optimum number of primary column stages An economic study of the number of primary column ideal stages depended on the grade of acetone produced by the primary column. The number of trays was varied between 20, where commercially worthless acetone was produced and 76, where high pressure liquid chromatography (HPLC) grade acetone was produced. As the secondary column was Table 4 Optimum primary column reflux ratios Methanol feed mole fraction

Optimum reflux ratio

0.25 0.50 0.75

3.5 3.5 4.2

Economic Potential ( £10^8)

350 6.000 5.500 5.000 4.500 4.000 3.500 3.000 2.500 2.000 1.500 1.000 0.500 0.000 0

P. Langston et al. / Chemical Engineering and Processing 44 (2005) 345–351

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 Number ofPrimary ColumnTrays

Fig. 9. Economic potential against number of primary column stages for the equimolar binary feed case.

not being altered the resultant methanol composition produced was constant, HPLC grade. The optimum number of stages is shown in Fig. 9 to be 73; which was expected as it was the minimum number of trays required to obtain the most valuable HPLC grade acetone. It was found that the optimum solvent feed stage, based on purity of top product, was stage 25, with the binary feed entering at stage 5. Hence with the optimum number of trays at 73, described above, there is a gap of 48 stages between the solvent feed and top column stage. This confirms the previous findings of Section 2.4 concerning the single column simulation where it was discovered that a large gap was required between solvent feed and the top tray, for both the single and split feed cases, so as to minimise the amount of solvent in the top product. This study did not account for variations of q, such as work previously [17]. However the reduced specific water consumption (q) achieved when placing the solvent feed towards the top of the column is economically insignificant when compared to the incurred costs required to remove the extra solvent impurity from the top product. Further simulations with equal-split solvent feed showed a negligible effect on the overall economic performance. Any reduction in capital cost due to one or two fewer trays would be offset by having more input streams.

4. Conclusions This study describes the simulation of three binary extractive distillation columns using the HYSYS computer software. The use of a particular fluid package (the Wilson package) enabled the successful simulation of the acetone–methanol, methyl acetate–methanol and methanol– chloroform separations. These simulations enabled the effect of the position of the solvent feed stream, split feed stream of solvent and the temperature of the solvent to be investigated. For the single column, single solvent feed cases, it was found that the concentration of the more volatile compo-

nent in the top product decreased with increasing solvent feed position for all three systems in agreement with previous studies [17]. The concentration of water also increased with increasing solvent feed position, meaning the optimum feed stages were found to be a trade-off of these conflicting effects. The binary and solvent feed temperatures and binary feed phase were found to have no effect on the overall separation achieved in any of the three systems considered, confirming previous experimental work [18]. For the split stream solvent feed cases, the concentration of methanol in the top product decreased with increased split feed distance, i.e. proximity of solvent feed to top tray. It was shown for both the single and split feed cases that a large gap was required between the solvent feed entry stage and the top of the column, to minimise the amount of solvent in the top product. A realistic recycle simulation with a secondary stripping column was successfully established for the acetone–methanol case. An economic analysis was subsequently performed indicating that one column design could optimally separate binary feed of varying composition between 25 and 75% methanol. In addition it was found that previous work [17], indicating that the solvent feed be positioned at the top of the column to be economically unviable in this simulation. A further economic analysis of the number of stages in the primary column indicated 73 ideal stages was the optimum number for an equimolar feed.

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