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Dynamic Process Intensification of Binary Distillation via Periodic Operation Lingqing Yan, Thomas F. Edgar, and Michael Baldea Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b04852 • Publication Date (Web): 20 Dec 2018 Downloaded from http://pubs.acs.org on January 7, 2019

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Industrial & Engineering Chemistry Research

Dynamic Process Intensification of Binary Distillation via Periodic Operation Lingqing Yana , Thomas F. Edgara , and Michael Baldeaa,b,* a McKetta b Institute

Dept. of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712

for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712 * Corresponding

author email address: [email protected]

Abstract This paper applies the concept of dynamic intensification (defined as changes to the dynamics, operation strategy and/or control of a process, that lead to a substantially more efficient processing path) to binary distillation columns. The resulting strategy consists of manufacturing a target product as a blend of two auxiliary products, both having lower energy demands than a reference value, which corresponds to producing the target product in a column operating at steady state. A discussion of the appropriate control structures and switching strategies between the two auxiliary products is provided. An extensive case study concerning the separation of a methanol - 1-propanol mixture was carried out, demonstrating that energy savings in the order of 1.4% are possible with no disruption in product quality or production rate. keywords: process intensification; dynamic intensification; distillation; energy efficiency

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Introduction

Distillation remains the workhorse separation technology for liquid mixtures in the chemical industry. More than two decades ago, Humphrey 1 estimated that about 40,000 distillation columns were in operation in the United States alone. This number has likely increased recently owing, e.g., to the availability of cheap hydrocarbon feedstock for chemical processing, and subsequent investments in production facilities. Distillation relies on the difference in boiling points between the components of a mixture to achieve separation. In other words, the mixture must be (at least partially) vaporized to be separated. Providing the required heat input (as dictated by the latent heat of vaporization) makes distillation highly energy intensive, with distillation operations accounting for an estimated 40% of the energy consumption of chemical plants 2 . The need to improve the economics of distillation processes has spurred significant research efforts. Here, we highlight the role of process intensification, as a design philosophy that emphasizes “doing more with less” 3 . In the realm of distillation, intensification led to the development and implementation of new configurations combining the functionality of two or multiple columns in a single device. The most prominent representative of this concept are dividing wall columns 3,4,5 . In a different vein, cyclic distillation 6 relies on separating the vapor and liquid traffic in the column such that mixing inefficiencies are reduced. However, cyclic distillation requires specialized hardware (column trays), which can come at considerable cost, particularly for retrofits. Motivated by this, in our previous work 7,8 , we introduced the concept of dynamic process intensification, which relies on operational changes (particularly, periodic operation), typically applied to existing hardware and equipment, with the goal of improving energy efficiency. Focusing specifically on distillation columns, in 7 we showed –for the first time, to our knowledge– that the static nonlinear characteristics of columns can be exploited to lower their average energy consumption. In this case, dynamic process intensification consists of, i) defining a target product (in terms of purity and production rate), ii) identifying two auxiliary products with lower reboiler duties than the target product and, iii) a periodic operation pattern that comprises switching production between the auxiliary products, such that the resulting blend has, on the average, the same properties at the target product but features lower energy use. ACS Paragon Plus Environment

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In this context, the dynamic intensification strategy outlined in 7 exploited the nonlinearity associated with the thermodynamic properties of a specific class of binary mixtures, where the more volatile (lower boiling point) component has a higher latent heat of vaporization than the less volatile component. This class of mixtures exhibits a favorable “negative gain” between distillate purity and reboiler duty (namely, in a specific operating range, duty may decrease as purity increases). We continue to seek and exploit this property in the present paper, which extends the dynamic intensification strategy to the distillation of a broader set of binary mixtures. We demonstrate that an appropriate choice of the column degrees of freedom and specifications can lead to similarly desirable nonlinear properties. We also show that the dynamic intensification strategy can be applied to obtain both the distillate and bottoms products at the same flow rate and purity but with lower energy use, compared to a reference distillation column operating at steady state. An extensive case study concerning the separation of a methanol - 1-propanol mixture is presented.

2

Motivating example

Let us consider the separation of a binary methanol-propanol mixture via distillation. Jacobsen and Skogestad 9 investigated this process considering an eight-stage column with an equimolar saturated liquid feed consisting of methanol and 1-propanol. The column operates at 1 bar condenser pressure with fixed 0.01 bar pressure drop per tray and sustained feed at 1.03 bar with 1 kmol/min flow rate. The eight equilibrium stages include a total condenser and a reboiler with feed at the fourth stage from top. Figure 1 shows the column configuration and the corresponding control loops (which will be discussed later in the paper). The system was simulated at steady-state in AspenPlusr

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using the Wilson equation to perform the

phase equilibrium calculations. In particular, a set of sensitivity studies of the distillate purity, yd , to the reboiler duty, QB , was carried out. Our investigations sought to delineate the effect of the product purity on reboiler duty under two operating regimes: fixed reflux rate and fixed boilup ratio. The results of the simulation studies (Figure 2) reveal an interesting feature, namely, that the ACS Paragon Plus Environment

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Condenser Psp PC

CW

Lsp FC

PI

L

Reflux Drum

2 stages above feed FC 1kmol/min methanol 50.0% propanol 50.0% 1.03bar 76.68 C

LC D

FT

Mixingtank 0.57 kmol/min methanol 87.4% propanol 12.6% 1.0bar 66.69C

Feedstage 3 stages below Column feed V

Sump

LC

B

FT Vsp

Reboiler

FC

Steamout

Steamin

0.43 kmol/min methanol 0.3% propanol 99.7% 1.07bar 98.49 C

Figure 1: Schematic column configuration choice of operating specifications has a strong impact on the static characteristics of the system. In effect, depending on these specifications, the (nonlinear) dependence between the aforementioned variables of interest can follow opposite directions. In order to quantify the difference between the static effects of the specifications, we define the system gain as follows:

K=

∆CV ∆yd = ∆QB ∆M V

(1)

which represents the ratio between changes in system input/manipulated variable QB (the “MV”) and corresponding changes in the output/controlled variable yd (the “CV”). The gain defined above provides useful insights in the energy consumption of the column, and can quantify the energy consumption effect of specifying the design distillate purity yD . To this end, an intuitive metric is to compare the change in QB for an increase and a decrease of equal magnitude in yD . Figure 2(a) shows that under fixed boilup ratio and changing reflux rate, the gain is monotonic and positive and its value is approaching zero as purity increases. In this operating case, ∆QB is higher for a unit increase in yd compared to a unit decrease. By contrast, Figure 2(b) shows ACS Paragon Plus Environment

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1

1 0.95

0.95

+ yd

+ yd 0.9

0.9 +E

yd

yd

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


0.85

-E 0.85 0.8

0.8

- yd

- yd 0.75

0.75 -E

0.7 4

6

8

10

0.7 11

12

Q B(GJ/h)

+E 11.5

12

12.5

13

Q B (GJ/h)

(a)

(b)

Figure 2: Nonlinear relationship for methanol-propanol ideal binary system. (a) Top purity versus reboiler duty under fixed boilup ratio = 6; (b) Top purity versus reboiler duty under fixed reflux rate = 5 kmol/min. Data are obtained from an eight equilibrium-stage column separating an equimolar saturated liquid mixture (feed flow rate = 1 kmol/min) a completely opposite but more economically interesting relationship. The gain is monotonic but negative. The magnitude of the gain also approaches zero as purity increases. Under fixed reflux rate (boilup ratio or vapor flow rate is allowed to change), a unit increase in yd can reduce reboiler duty (and save energy) by a larger amount than the duty increase when decreasing yd by one unit.

3

Dynamic Intensification Concept

The arguments above and the data in Figure 2(b) suggest a new dynamic operating paradigm for binary distillation columns, that has the potential to generate a desired product slate (defined in terms of bottom and top product purities and flow rates) at a lower energy consumption than an equivalent column that is operated at a single steady state. We rely on Figure 3, which provides a generic representation of the relationship revealed by Figure 2(b), to explain this concept. Let us assume that the desired purity of the top product is CV ∗ . Based on the corresponding value CV ∗ of the controlled (output variable), and using the monotonicity of the relationship between the manipulated and controlled variables, the corresponding value M V ∗ of the input can be computed. The desired product can thus be uniquely


5


1

1

CV1

*

CV

yd

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

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*

CV2

2

0.9

0.8 11 MV1

MV*

12

MV2

12.8

Q B (GJ/h) Figure 3: Conceptual representation of energy saving via operational changes. Target CV ∗ can be achieved by switching between CV1 and CV2 based on their corresponding M V1 and M V2 defined in terms of the pair Π∗ , (M V ∗ , CV ∗ ). Observe that a product with purity CV ∗ can either be manufactured by operating the column at steady-state at (M V ∗ , CV ∗ ), or by mixing two auxiliary products, Π1 , (M V1 , CV1 ) and Π2 , (M V2 , CV2 ). The two auxiliary products are produced by the same column at two other, different operating points, and are obtained by alternating periodically between the two corresponding operating points. The auxiliary products are then blended in a (sufficiently large) tank, such that, on the average the blended product reaches the desired specification CV ∗ . Possible off-specification product made during the dynamic transition can also be mixed inside the tank. In our previous work 7 , we postulated that ideal auxiliary product candidates should both have a lower “cost” (defined in terms of the manipulated variable) than M V ∗ to guarantee a timeaverage lower energy cost of the product with purity CV ∗ . The relationship presented in Figure 2(b) indicates that such ideal candidates do not exist for this binary mixture. Nevertheless, the figure suggests that energy savings could still be achieved by producing a product with purity CV ∗ as a blend of products with purities CV1 and CV2 , given that the energy consumption for the high purity product is lower than the energy consumption of the low purity product, i.e., CV1 > CV2 while M V1 < M V2 . The corresponding mixture can be defined based on the split coefficient α, ACS Paragon Plus Environment

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which dictates the proportion of operating points Π1 , (M V1 , CV1 ) and Π2 , (M V2 , CV2 ) required to obtain the desired CV ∗ :

CV ∗ = αCV1 + (1 − α)CV2

(2)

The implementation of the proposed dynamic intensification approach can be described via the following procedural steps: (S1) Establishing the operating specifications of the column in terms of a fixed reflux rate for a given product slate (product purities and flow rates) (S2) Defining the two auxiliary products, such that the corresponding “quality variables” (product purities) are higher and, respectively, lower than that of the target products (S3) Computing the split ratio α (S4) Implementing a control system capable of transitioning effectively between the operating points corresponding to the two auxiliary products, and defining a switching scheme that imposes the split ratio α between the products, such that, on the average, the purity of the blended products meets the product slate specifications. Below, we present several general remarks and potential challenges to the above developments; these will be further elaborated in the case study presented later in the paper. First, the premise of the proposed dynamic intensification approach is counterintuitive given the common wisdom that increasing purity requires a higher energy consumption (also as indicated by the results shown in Figure 2(a)). However, the results in Figure 2(b) can be understood by considering the converse perspective, that energy can be saved by sending less vapor towards to top of the column. Second, Figure 4 shows that the distillate flow rate drops monotonically as purity increases under constant reflux operation. This drop is particularly noticeable as the purity of the distillate increases. Thus, the proposed strategy can experience difficulties in maintaining separation performance. Changes in the vapor flow rate, while reducing energy consumption, may result in a ACS Paragon Plus Environment

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higher purity distillate that is produced at a low flow rate, and the loss of production rate could result in a product that in fact requires a higher per unit energy expenditure than the product obtained during steady-state operation. In turn, this suggests that one or more additional degrees of freedom (and control loops) should be employed in Step (S2) (and later in Step (S4)) to ensure that the desired production rate is met while also meeting quality constraints. Third, the proposed approach may present control difficulties, as it entails operating at high purities for at least part of the time 11,12 . Fourth, a major benefit of this strategy is that it relies on exploiting the nonlinear features of existing equipment designs, rather than on specialized equipment as is the case with cyclic distillation (discussed above). As a consequence, the capital expenditure required to implement it is low, and involves the purchasing and installation of the blending tanks required to “average out” the product quality.

4

Case Study: Dynamic Intensification of Methanol-Propanol Distillation

Below, we rely on the motivating example presented earlier to illustrate the dynamic process intensification concepts introduced above.

4.1

Steady-state considerations

In order to carry out Steps (S1) and (S2), we must choose a feasible location of yd∗ (CV ∗ ) in Figure 2(b) and the corresponding two auxiliary products. Figure 2(b) was generated based on a fixed 5.0 kmol/min reflux rate, and the column is designed to reach methanol purity yd∗ = 87.36%. We will maintain this value as the product purity target for periodic operation. In our previous work 7 , we showed that increasing the product purity (naturally) led to a drop of product flow rate (in that case, distillate). A second manipulated variable (pressure) was utilized to compensate for this effect, and it was shown that the nature of the binary mixture was such that higher pressure increased flow with lower energy consumption. ACS Paragon Plus Environment

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1 0.95 0.9

yd

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


0.85 0.8 0.75 0.7 10

20

30

40

Distillate (kmol/h) Figure 4: Nonlinear relationship between distillate flow rate and product purity for methanolpropanol binary system follows the same trend as purity versus reboiler duty In the present case, utilizing pressure as a secondary manipulated variable M V 0 may assist with increasing distillate flow rate but may compromise energy savings (Figure 5). As a consequence, we utilize reflux flow rate as a third manipulated variable, M V 00 , which can be used to maintain the desired product flow rates if applied in-phase with pressure changes, due to its direct influence on both distillate flow rate and purity. Thus, the auxiliary operating points can be defined in terms of the corresponding reflux, vapor flow rates and operating pressure. Consider the dependence of the distillate flow rate on the reflux rate shown in Figure 6. Focusing particularly on the high purity operating regime (which was the operating region of concern in our previous arguments), we note that for a given distillate composition, the distillate flow rate can slightly increase for a slight increase in reflux rate. Interestingly, the distillate flow rate may also slightly increase for a slight drop in reflux. The behavior of the reboiler duty is as expected, where duty increases as the reflux rate increases. Next, the auxiliary products are defined. In general, we seek a combination of an auxiliary Π1 , whose high purity compensates for the drop in production rate, and an auxiliary Π2 whose concentration should be lower than but close to that of Π∗ , such that its contribution to energy savings is high without having a significant negative impact on the quality of the blended product. The choice of auxiliary products is shown in Table 1, where the steady-state operating paramACS Paragon Plus Environment

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34.275 34.27

QB (GJ/h)

Distillate (kmol/h)

12.4

-0.05 bar 1.0 bar +0.01 bar

34.28

34.265 34.26

12.35

12.3

34.255 34.25 0.8732

12.25 0.8734

0.8736 Yd

0.8738

0.874

0.86

0.87 Yd

0.88

Figure 5: The effect of pressure on the distillate flow rate (left) and reboiler heat duty (right) under fixed reflux rate = 5 kmol/min

13.5 4.9 kmol/min 5.0 kmol/min 5.1 kmol/min

31.9 31.8

13 QB (GJ/h)

Distillate (kmol/h)

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

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31.7

12.5 12 11.5

31.6 11 31.5 0.936

10.5 0.938

0.94 yd

0.942

0.944

0.7

0.8

0.9

1

yd

Figure 6: The effect of reflux rate on the distillate flow rate at high purity (left) and reboiler heat duty (right) at operating pressure of 1 bar


10

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Table 1: Operating points for desired product CV ∗ , the auxiliary products, and the corresponding weighted average values for the blended product. The values for Π1 and Π2 are presented in terms of deviations from Π∗ .

yd FM ethanol kmol h Distillate kmol h xb FP ropanol kmol h Bottoms kmol h P bar Vapor kmol h Reflux kmol h QB GJ h QC GJ h Total duty GJ h

Π∗

Π1

Π2

0.8737 29.93 34.26 0.9974 25.67 25.74 1.00 296.3 300.0 12.30 -12.23 24.53

+0.0644 -0.0739 -2.433 -0.0024 +2.360 +2.434 +0.01 -1.886 +6.00 -0.0829 0.0919 -0.1748

-0.0023 +0.0039 +0.0939 +0.0001 -0.0899 -0.0939 -0.05 -5.267 -6.00 -0.1807 0.1805 -0.3613

Weighted average (Π1 and Π2 ) 0.8737 29.93 34.26 0.9975 25.67 25.74

% difference Π∗ vs. weighted average O(10−9 ) 0.004 0.004 0.004 -0.001 -0.005

291.1 294.4 12.12 -12.05 24.17

-1.736 -1.853 -1.357 -1.363 -1.445

eters are listed along with a comparison between weighted average properties of Π1 and Π2 (which would reflect the blended product obtained from periodic operation) and these of Π∗ . The split coefficient is specifically calculated based on equation (3) and calls for weighting the high purity operating point (Π1 ) with α = 0.037 (i.e., spending 3.7 % of the operating time at this point), while the low purity operating point Π2 by 1 − α = 0.963, a result consistent with the arguments presented in the previous paragraph. The three operating points are represented graphically in Figure 7. Based on steady-state arguments, dynamic intensification via periodic operation can potentially result in 1.44 % energy savings with minimal impact on distillate and bottom product qualities, compared to producing a product of purity and flow rate corresponding to Π∗ at steady state.

yd∗ =

4.2

αD1 yd1 + (1 − α)D2 yd2 αD1 + (1 − α)D2

(3)

Dynamics an control considerations

A multi-loop linear control system was implemented with the purpose of imposing transitions between the operating regimes corresponding to the two auxiliary products. The control loop pairings were as follows: the vapor boilup rate was adjusted by the steam flow rate to the reboiler


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(and effectively the heat duty QB ), while the reflux rate was adjusted by the reflux valve. The column pressure was controlled by manipulating the coolant flow rate, while the levels of the sump and distillate drum were stabilized by manipulating the bottoms and distillate flow rates, respectively. It is important to emphasize that this control setup serves the sole purpose of studying the feasibility and effect of imposing the transitions between the auxiliary operating points. It is not intended to track the product purity setpoint or to reject disturbances. An additional, supervisory, control system would be required to track product purity in closed-loop. The design of this system must account for the fact that the relationship between QB and distillate purity yd becomes nonlinear and nonmonotonic when the auxiliary operating points are defined in terms of both the reflux flow rate and the vapor boilup/reboiler duty. This point is well-illustrated by comparing Figures 2b and 7a, which shows the multiplicity between input QB and output yd . Note also that the mission of this supervisory control system is facilitated by the blending tanks installed for the distillate and bottoms products (Figure 1), which act as buffers that attenuate the impact of any disturbances that affect composition.

4.3

Dynamic simulation results

A dynamic simulation study was carried out to verify the steady-state results presented in Table 1 above. The steady state results rely on the key assumption that the process can be continuously run at the desired operating points for a well-defined amount of time and does not account for the transient effects that are inherent to periodic operation. Thus, the purpose of the dynamic simulation study is twofold: • Confirm the ability of the control system to reach and maintain the steady-state operating points corresponding to the two auxiliary products and ascertain that the steady-states of the dynamical system correspond (notably, in terms of purity and flow rate) to those specified in the steady-state analysis. • Demonstrate the feasibility of rapidly switching between the two steady-state operating points ACS Paragon Plus Environment

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0.94 1

yd

0.92

0.9

*

0.88

2

12.1

12.15

12.2 12.25 QB (GJ/h)

12.3

(a) Mole fraction of methanol in distillate versus reboiler duty 297

*

296 V(kmol/h)

295 1

294 293 292

2

291 290 12.1

12.15

12.2 12.25 QB (GJ/h)

12.3

(b) Vapor boilup rate versus reboiler duty 0.94 1

0.92 yd

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


0.9

0.88

290

* 2

292

294 V(kmol/h)

296

(c) Mole fraction of methanol in distillate versus vapor boilup rate

Figure 7: Switching between the desired product and auxiliary products in Table 1 ACS Paragon Plus Environment

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As emphasized above, we are not interested in tracking product purity per se or in studying the disturbance rejection capabilities of the control system. A study of these important characteristics (along with the design of the aforementioned supervisory control system) is planned as future work. The dynamic simulation study was carried out as a flow-driven dynamic simulation using Aspen Dynamics V8.8. A Visual Basic for Applications (VBA) script in Microsoft Excel, communicating with Aspen Dynamics via ActiveX, was used to impose the setpoint changes required to switch between the auxiliary operating points Π1 and Π2 . The setpoints were imposed as square-wave signals; for producing the product with high purity CV1 , the vapor flow rate was set to 294.371 kmol/h, with a reflux rate of 306 kmol/h. During the low purity period (Π2 ), the vapor flow rate was switched to 290.990 kmol/h with reflux rate at 294 kmol/h. The actual length of each operating period was calculated based on the dynamic performance of the system evaluated with regard to operating at the high purity state. Operation at the high purity state was assumed to be complete when the steady state (defined as the distillate purity being within 0.03 % of the value for CV1 from Table 1) was reached. The time constant of the column under consideration (calculated as the ratio between liquid holdup in the sump and condenser drum, and feed flow rate) is about 0.17 h. The dynamic simulation results indicated that the above (admittedly restrictive) steady-state condition was reached in a time interval spanning about 16 time constants. For the purpose of simulating periodic operation, the simulation was extended for one more time constant (total 17 time constants) at Π1 , after which the relevant setpoints were switched to the values corresponding to Π2 for the amount of time indicated by the split fraction α. Thus, each cycle of the dynamic simulation consists of maintaining setpoints at values corresponding to Π2 for 76.51 h then switching to Π1 for 2.91 h as shown in Figure 8. Time-average data for three consecutive cycles are shown in Table 2, confirming the fact that periodic operation can achieve the energy savings and product purity targets predicted by the steady-state analysis.


14

distillate bottoms

40

35

30

25 0

0.94

0.92

0.9

0.88 0

50

100

150

200

50

100 150 Time(h)

200

250

24.35

24.3

24.25

24.2

0

50

100

150

200

Time(h)

Time(h)

(c) Purity of methanol distillate

(d) Total energy consumption

Figure 8: Simulation of switching between operating at Π1 and Π2 .

Table 2: Dynamic simulation results for three cycles (time-average shown)

yd FM ethanol kmol h Distillate kmol h xb FP ropanol kmol h Bottoms kmol h V kmol h L kmol h QB GJ h QC GJ h Total duty GJ h

250

(b) Distillate and bottoms flow rates Total energy consumption(GJ/h)

(a) Reflux rate and vapor flow rate

yd

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


Production flow rate(kmol/h)

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Time-average (Π1 and Π2 ) 0.8736 29.92 34.25 0.9975 25.68 25.74 291.1 294.4 12.12 -12.05 24.17

Π∗

% difference

0.8737 29.93 34.26 0.9974 25.67 25.73 296.3 300.0 12.30 -12.23 24.53

-0.007 -0.031 -0.024 0.006 0.019 0.013 -1.742 -1.873 -1.445 -1.452 -1.448


15

250


4.4

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Impact of transition times and choice of switching frequency

In order to define the impact of transitions on the proposed periodic operation strategy, we define the transition time as the amount of time required by the process variables to be within 0.05 % of the target value for Π1 or Π2 , once a setpoint change (from, respectively Π2 or Π1 ) has been initiated. Transition times were extracted from the previous simulation data: due to the nonlinearity of the system, the system requires 2.52 h to transition from Π2 to Π1 , while the transition from Π1 to Π2 takes 1.69 h. The transitions do not have the same impact on the time-average performance of the system. Simulation of a single cycle (with a 2.91h switching time) is shown in Figure 9. It suggests that the (longer) transition from Π2 to Π1 impacts performance negatively: purity is always below Π1 target, and the distillate rate is predominantly below its Π1 steady state value. On the other hand, the (shorter) transition from Π1 to Π2 has qualitatively the opposite effect. Quantitatively, however, the impact of this latter transition is not sufficient to offset the performance penalty incurred by transitioning from Π2 to Π1 . These findings suggest that transition times should be accounted for when designing the periodic operation policy. The switching frequency must consider transitions in addition to following the split ratio α prescribed by the steady state analysis, and requires a dynamic optimization calculation which is planned as future work. We examined the effect of the choice of switching frequency via simulation. Considering the 2.52 h (about 14.8 time constants) transition time from Π2 to Π1 as the fastest possible switching time, we carried out simulations with gradually decreasing switching frequencies (94%– corresponding to the example in the previous subsection – , 50% and finally 10% of this value), which were imposed by correspondingly increasing the switching times. Table 3 lists the deviation in performance and cost with respect to Π∗ for each case, time-averaged for three cycles. The results indicate –not unexpectedly– that decreasing the switching frequency leads the system to more closely mimic the results of the steady-state analysis; equivalently, the impact of transitions on overall time-average system performance is attenuated if the transitions are less frequent. Finally, we note that the initial conditions of the system also impact its time average perforACS Paragon Plus Environment

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P sp

310

0.98

P L sp

305

0.96

L V sp

300

0.94

V 295

0.92 0.9

Flow rate (kmol/h)

Condenser pressure (bar)

1

Production flow rate(kmol/h)

315

1.02

distillate bottoms

40

35

30

25

290 0

2 Time (h)

4

6

0

0.94

0.92

0.9

0.88 0

2

4

1

2

3 Time(h)

4

5

6

(b) Distillate and bottoms flow rates

6

Total energy consumption(GJ/h)

(a) Reflux rate and vapor flow rate

yd

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


24.35

24.3

24.25

24.2

0

2

4

6

Time(h)

Time(h)

(c) Purity of methanol distillate

(d) Total energy consumption

Figure 9: Simulation of dynamic switching between Π1 and Π2 , operating for 2.91h at each point.

mance if the number of cycles/switches is small. This is the case with all periodic operations, and the effect largely disappears once the system goes through a sufficient number of cycles and reaches a periodic steady state. We verified that the distillation column considered here does indeed reach what can be construed as a periodic steady state based on six switching cycles, as illustrated by the phase plane plot shown in Figure 10.


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0.94

1

0.92 yd

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

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0.9 (34.26, 0.8737) 0.88

* 2

25

30 35 Distillate (kmol/h)

40

Figure 10: Cyclic steady state operation

Table 3: Time average (over three cycles) percent deviation of key process variables from Π∗ as a function of switching frequency yd Distillate kmol h xb Bottoms kmol h QB GJ h

10 % of base case -0.007 +0.008 0.005 -0.013 -1.446

50 % of base case -O(10−4 ) -0.016 0.006 0.010 -1.444

94 % of base case -0.007 -0.024 0.006 0.013 -1.445


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100 % of base case -0.008 -0.028 0.006 0.016 -1.445

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5


Conclusions and future work

We presented a dynamic intensification strategy for binary distillation columns. The underlying principle consists of manufacturing the target product as a blend of two auxiliary products, both having lower energy demands than the value corresponding to producing the target product in a column operating at steady state. We demonstrated that this is a viable strategy for a binary column provided that an appropriate choice of the column degrees of freedom and specifications is made. For the near-ideal mixtures considered, this amounted to specifying the reflux rate and boilup/vapor flow rate and operating pressure). An extensive case study concerning the separation of a methanol - 1-propanol mixture was carried out, demonstrating that energy savings in the order of 1.4% are possible with no disruption in product quality or production rate. This is an important result given that, i) it is obtained with only operational changes (i.e., no dedicated column hardware is required) and, ii) it can potentially be replicated to a large fleet of operating columns. The proposed framework is still at the conceptual stages but provides motivation and impetus for future work aimed towards its practical implementation. Future efforts will include, i) (dynamic) optimization studies for determining the most appropriate auxiliary products and switching strategies, ii) devising supervisory control algorithms that ensure product purity tracking in the face of disturbances, and, iii) developing screening tools for determining mixtures where distillation is amenable to dynamic intensification.

6

Acknowledgements

Funding from the US Department of Energy through the RAPID Process Intensification Institute under award DE-EE0007888-05-4 is acknowledged with gratitude.

Disclaimer This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their


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employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

References [1] Humphrey, J. L. Separation processes: playing a critical role. Chemical Engineering Progress 1995, 91 . [2] Isopescu, R.; Woinaroschy, A.; Draghiciu, L. Energy reduction in a divided wall distillation column. Revista de Chimie 2008, 59, 812–815. [3] Reay, D.; Ramshaw, C.; Harvey, A. Process intensification: engineering for efficiency, sustainability and flexibility; Butterworth-Heinemann: Waltham, MA, 2013. [4] Madenoor Ramapriya, G.; Tawarmalani, M.; Agrawal, R. Thermal coupling links to liquidonly transfer streams: A path for new dividing wall columns. AIChE J. 2014, 60, 2949–2961. [5] Donahue, M. M.; Roach, B. J.; Downs, J. J.; Blevins, T.; Baldea, M.; Eldridge, R. B. Dividing wall column control: Common practices and key findings. Chem. Eng. Proc.: Process Intensification 2016, 107, 106–115. [6] Maleta, B. V.; Shevchenko, A.; Bedryk, O.; Kiss, A. A. Pilot-scale studies of process intensification by cyclic distillation. AIChE Journal 2015, 61, 2581–2591, DOI: 10.1002/aic.14827. [7] Yan, L.; Edgar, T.; Baldea, M. Dynamic Process Intensification of Binary Distillation Based on Output Multiplicity. AIChE J. DOI: https://dx.doi.org/10.1002/AIC.16506. ACS Paragon Plus Environment

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[8] Baldea, M.; Edgar, T. Dynamic process intensification. Curr. Opinion. Chem. Eng. DOI: http://dx.doi.org/10.1016/j.coche.2018.08.003. [9] Jacobsen, E. W.; Skogestad, S. Multiple steady states in ideal two-product distillation. AIChE Journal 1991, 37, 499–511, DOI: 10.1002/aic.690370404. [10] AspenTechnology, Aspen Plus V8.8. www.aspentech.com. [11] Skogestad, S.; Morari, M.; Doyle, J. C. Robust control of ill-conditioned plants: High-purity distillation. IEEE Trans. Automat. Contr. 1988, 33, 1092–1105. [12] Baldea, M.; Daoutidis, P. Dynamics and Nonlinear Control of Integrated Process Systems; Cambridge University Press: Cambridge, UK, 2012.


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Figure 11: TOC graphic


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Dynamic Process Intensification of Binary ... - ACS Publications

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